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Zambia has recently faced prolonged load shedding due to reduced hydroelectricity generation caused by unfavourable rainfall patterns. To tackle this issue and satisfy increasing energy demands, the government aims to explore alternative energy sources like wind energy. However, the uncertainty surrounding the economic feasibility of implementing wind power projects in Zambia poses a significant challenge. This study evaluates the economic feasibility of eight proposed wind farm sites using net present value (NPV), simple payback period (SPP), internal rate of return (IRR), and levelized cost of electricity (LCOE). By examining energy yield analysis (EYA), wind speed, and financial indicators, the study identifies the most economically viable wind farm site(s). The results reveal that the Lusaka wind farm is the most economical, with an energy yield analysis of 386 GWh, wind speed of 8 m/s, NPV of USD 316 million, SPP of 2.9 years, IRR of 82%, and LCOE of 0.182 USD/kWh. An economic sensitivity analysis, varying the average electricity tariff, also points to Lusaka as the most financially viable site. Consequently, policymakers are advised to develop cost-reflective feed-in tariff (FiT) schemes and power purchase agreements to establish electricity tariffs that encourage investment from independent power producers in Zambia.

Abstract Grid integration constraints are limiting the deployment potential of renewable energy sources in Malta. Large penetrations of photovoltaics in the low voltage (LV) distribution network pose a significant risk to grid stability due to their inherent intermittency and are known to cause overvoltages and reverse power flows. The authors evaluate how self‐consumption strategies with distributed battery energy storage systems can contribute to the voltage regulation in LV networks and the reduction of reverse power flows. The batteries are controlled to absorb the reverse power flow at the dwellings' point of common coupling, before this is injected into the LV network. Simulations show that uncoordinated strategies are not suitable to address the distribution network challenges during reverse power flows and evening peak demands. On the other hand, self‐consumption coordinated by a time‐varying feed‐in tariff (FiT) can provide higher profitability to the prosumers while providing added benefits to the utility. The net‐billing profitability for the prosumers in a self‐consumption scenario with time‐varying FiT is transformed from the downward trend of the uncoordinated scenario to an upward trend against the increasing values of storage capacity.

Automated Vehicles (AVs) are expected to reduce CO2 emissions and energy demand of road transportation by mechanisms such as more stable vehicle control, but realisation of these benefits depends on AV deployment and use. Simulation studies have reported a wide range of potential impacts, depending on the driver model and assumptions. Studies have focused on the total impact on CO2 emissions in specific traffic volume and speed limit conditions and have not separated impacts for different road users. Heavy-duty vehicles (HDVs) have often been omitted entirely. This study assessed the potential impacts of conditionally automated driving on the CO2 emissions and energy demand of equipped and unequipped passenger cars (MVs) and unequipped HDVs with a systematic approach, covering different speed limits, traffic volumes and AV penetration rates on motorways. The methodology incorporated traffic microsimulation, an emissions calculation tool, and a formula for tractive energy demand. Replacing passenger cars with AVs in traffic simulation affected emissions of all road users, and the magnitude and direction of impacts differed between vehicle types. Whereas average energy demand and CO2 emissions of AVs were lower in most conditions compared to MVs at baseline, benefits for all vehicle types were seen only at the highest traffic volumes. Changed traffic dynamics can lead to increases in energy demand and emissions of HDVs and MVs already at low AV penetration rates in moderate traffic. Thus, future studies on AV impacts should include more variation in simulated conditions and consider impacts on different vehicle types separately.

The increasing industrial development and energy demand have necessitated the maximization of available energy use and the deployment of renewable resources. Effective energy management, optimal modeling, and efficient planning are essential to transform the power system into a high-efficiency, optimal model. This study focuses on the initial step of modeling smart buildings (SBs) equipped with non-responsive devices and renewable photovoltaic sources. A comprehensive energy management (EM) plan is formulated for these buildings, incorporating the KNX protocol for solar energy system management. Batteries are integrated into the building model to store energy during periods of low consumption and serve as generators during peak load conditions, with the primary goal of minimizing power system losses and related costs. To address the complexity of this model, whale optimization algorithm (WOA) is employed for optimization. Optimal candidate buses are selected for interconnected building management based on a suggested sensitivity analysis to minimize losses. Cost performance is then assessed, considering energy production and sales. The findings indicate that substantial control of operating costs can be achieved through strategic management of battery charging and discharging, as well as the utilization of photovoltaic units. The proposed model is evaluated across various scenarios using a test system comprising 30 modified system, demonstrating its effectiveness in enhancing energy efficiency and management.

How can people imagine ways of achieving desirable energy futures in cities, oriented towards sufficiency? Building on the notion of sufficiency understood as avoiding demand while meeting human needs, this paper discusses the results of seven participatory workshops (n = 154 participants) held in Switzerland where new imaginaries around the future in cities were discussed. The results demonstrate that people can reflect on how living, consuming and working in cities could achieve the double dividend of meeting needs while using less energy. Based on the notion of practices-as-satisfiers, the participants collectively discussed synergic satisfiers or changes to practices were found to lead to energy savings and the satisfaction of multiple needs. However, for this to be possible, practices need to be thought of as part of systems: several changes would need to take place at once. Certain practices, such as reducing work time, are prefigurative of others. Participants reflected on how desirable imaginaries could be planned for, moving from individual change to proposed collective changes, considering their interrelated and prefigurative nature. The findings show how similar forms of public participation can lead to policy-relevant insights for planning, adopting and implementing sufficiency measures in cities. Policy relevance While efficiency and renewable energy measures are central in many national energy strategies, sufficiency policies—involving reductions in energy demand together with wellbeing—are emerging as a key component in the energy transition. The results of participatory workshops conducted with Swiss citizens provide insights into how to organize towards sufficiency in the future. First, policy development must consider sufficiency as part of the goal, and reflect on the means of reaching this goal. Second, people should be included in the debate, in that they can reflect on collective changes that must take place today to achieve desirable sufficiency futures. The results presented around synergic, interrelated and prefigurative nature of systems of practices and on changes needed are useful to inform debates on what could come out of such collective discussions, namely interrelated measures imagined at the collective level towards sufficiency.

Pakistan is faces significant challenges in meeting its energy demand and consumption needs for consumers. This country's energy production from primary sources such as petroleum and natural gasses is incompetent in fuel-use and hence unable to meet feasibility cost. With an increasing population, Pakistan's energy consumption per capita has been steadily rising. This behaviour is leading to critical energy issues, especially in remote rural areas. This trend in rising energy costs and demand factors are similar to those in the energy markets in the South and South-East Asia. The primary energy sources in Asia continent, including fossil fuels, are insufficient supply to meet this growing demand in production and thus resulting in frequent electricity blackouts. Consequently, renewable energy sources such as solar photovoltaic (PV) and wind power have substantially started to produce energy and to provide a huge portion of Pakistan's daily energy needs apart in conventional energy currently. However, these sources are not yet as reliable, conventional energy bases have a challenge for sustainable energy production. As a result, renewable energy factors nonetheless initial started have effectively stabilized energy consumption, particularly for green electricity with net-zero carbon emissions. The aim of this study is to evaluate the feasibility and cost-effectiveness of integrating a microgrid hybrid system with combined (solar PV/wind power) renewable energy as well as conventional fossil fuel generators. This evaluation focuses on predicting energy production and its costs using Hybrid Optimization of Multiple Energy Resources (HOMER) software, and to enhance the electricity standards at NUST (National University for Sciences and Technology), Pakistan. The proposed methodology of microgrid hybrid system, when evaluated using HOMER software, shows a significant improvement in energy stability and cost efficiency. Moreover, this proposed system can reduce reliance on fossil fuels by a substantial percentage, enhances the predictability of energy production, and optimizes its energy consumption. These can achieve better performance metrics in terms of reliability, cost, and environmental impact; feasible solution for Pakistan and the developing countries. This proposed methodology offers a novel approach by integrating renewable energy sources with conventional generators to create a balanced and efficiency factor by microgrid system. This hybrid system goals as an investigation is to optimize this energy production, reduce carbon emissions, and provide a more stable and cost-effective energy supply.

The energy and industrial sectors' most recent extraordinarily quick expansion has resulted in a substantial rise in stationary CO2 sources. As a result, many questions have been raised concerning how to stop global warming and implement methods to mitigate its effects by 2050 to create a low-carbon and sustainable future. Over the last three decades, India's CO2 emissions have increased at a growth rate of 3.1 % mainly consumption of fossil fuels. In this global and Indian situation, the best method, besides renewable energy sources, is Carbon capture, utilization, and storage (CCUS), which will make it possible to achieve net-zero emissions targets within this century. Given this, a brief review and discussion that aims to study India's development in CCUS deployment and its importance for sustainable development were conducted. The review highlights the current energy scenario of India and the world, the CO2 increase trend, and its consequences globally. Furthermore, a brief discussion on the present status of several areas of carbon capture, utilization, and storage (CCUS) technologies have been done. To clearly define the maturity of each important component of the CCUS system with a commercialization direction route, we focused on determining the technological readiness level (TRL) from India's perspective. Further, we identified important data of different sizes from the biggest R&D schemes, policies, regulations and initiatives taken by the government across the country. Lastly, our study seeks to provide a guide for the successful implementation of CCUS in India.

Abstract Developing comprehensive scenarios for the shipping sector has been a challenge for the Integrated Assessment Model (IAMs) community, influencing how attainable decarbonization is in the sector, and for Earth System Models (ESMs), impacting the climate contribution of shipping emissions. Here we present an approach to develop spatially explicit energy demand projections for shipping in alignment with the Shared Socioeconomic Pathways framework and IAMs projections of global fossil fuel demand. Our results show that shipping could require between 14 and 20 EJ by 2050, corresponding to a 3% and 44% increase from 2018 for the SSP1-1.9 and SSP3-7.0 scenarios. Furthermore, the energy projections we present in this publication can be combined with different fuel mixes to derive emission inventories for climate modeling and, thus, improve our understanding of the various challenges in mitigating emissions for shipping. Through that, we aim to present a framework to incorporate detailed spatial shipping inventories and increase transparency for the scientific community.

Abstract Background The transport sector in South Africa is responsible for around 11% of the country's carbon dioxide emissions, with road transport contributing an overwhelming 90% of this total, as noted by the South African Green Transport Energy of South Africa. As part of its commitment to global climate pacts, South Africa aims to reduce emissions from its road transport sector. Yet, studies focused on reducing energy consumption and related emissions in this sector have been sparse. Results Utilizing a bottom-up accounting modelling framework Low Emissions Analysis Platform (LEAP), this research investigated five low-carbon transition scenarios alongside a business-as-usual (BAU) scenario for road transport. These scenarios comprised Fuel and Technology Switching (FTS), Modal Shift (MS), Logistics Improvement (LI), Energy Efficient (EEF), and a Combined Mitigation (CMT). The BAU scenario was established as a benchmark to demonstrate energy demand and emissions in the absence of changes to current practices or policies. According to our model, under the BAU scenario, there will be a 61% surge in final energy demand, from 769 petajoules (PJ) in 2020 to 1240 PJ by 2050, accompanied by a proportional increase in emissions. The study revealed that the implementation of any of the alternative low-carbon scenarios could yield a reduced energy demand by 2050. LI 21%, MS 33%, FTS 40%, EEF 48%, CMT 77%, Significantly, a combined approach, integrating multiple low-carbon policies, can achieve more substantial reductions in energy demand and Carbon Dioxide (CO2) emissions than applying single policies separately. Conclusions This study emphasizes the importance of crafting province-specific solutions, acknowledging that challenges and contexts vary between provinces. Furthermore, lessening energy reliance not only diminishes the nation's fuel import bills but also improves air quality and aids in achieving low emission targets.

The building sector is one of the largest energy user and carbon emitters globally. To increase the utilization rate of renewable energy and reduce carbon dioxide emissions, the optimal technical scheme of active public institutions and coupled utilization of renewable energy is studied. In this study, the energy consumption of three types of public institutions in various regions of China was simulated by using DeST building energy consumption software, combined with energy conversion efficiency and data released by the National Bureau of Statistics, and the total energy demand and total energy supply of public institutions were predicted using the load density method. Based on the coupling mechanism of the MARKAL model, the optimal proportion of renewable energy in the energy supply of public buildings in different regions is determined. Through the study of the number of public institutions in various regions of China, energy consumption characteristics, construction area, and other related data, the reverse energy flow method is creatively proposed, and the active and renewable energy coupling algorithm from the energy demand side of public institutions to the energy supply side is established. The results show that the central region has the highest utilization rate of renewable energy in the public sector, reaching 36.18%. The use of renewable energy in public buildings in hot summer and warm winter zones decreased to 35.08%, and it was 12.82% in cold zones. By 2025, the proportion of renewable energy resources in China is expected to reach 29.2%. The energy coupling model and algorithm constructed in this paper can provide a basis for the coupling macro configuration of renewable energy in public institutions in China.

With rapid global economic growth and a rise in disposable household income, particularly within a progressively warming planet, the escalating demand for energy to achieve thermal comfort has become a salient concern in the Global South, notably in emerging economies like India. This burgeoning need for cooling solutions has not only underscored the vital role of energy consumption but has also accentuated the imperative of comprehending the ensuing implications for electricity policy and strategic planning, particularly within the ambit of the Global South. This study explored the nuanced landscape of active cooling within an intentional community, Auroville, in southern India, aiming to discern the factors underpinning household preferences and practices in the pursuit of thermal comfort. Employing a mixed-methods approach, this study contributed empirically and methodologically to the interdisciplinary discourse by analysing residential electricity consumption patterns and cooling practices within selected households in the specified community. The study unfolded in three methodological stages: firstly, an analysis of climatic data coupled with an environmental stress index (ESI) assessment; secondly, the monitoring of end-user electricity consumption followed by rigorous data analysis; and lastly, the utilisation of qualitative in-depth interviews and observational techniques. This study’s outcome yielded empirical insights into the unprecedented shifts in the ESI for Auroville since 2014. Furthermore, the study unravelled the intricate complexities inherent in occupant behaviour within residential structures, thereby offering valuable insights into the practices that shape householders’ cooling preferences. This research enriched the understanding of the dynamics of energy consumption in the pursuit of thermal comfort and contributes to the broader discourse on sustainable development and energy policy in the context of climate change.

In an effort to mitigate anthropogenic climate impacts the U.S. has established ambitious Nationally Determined Contribution (NDC) targets, aiming to reduce greenhouse gas emissions by 50% before 2030 and achieving net-zero emissions by 2050. Enhanced rock weathering (ERW)—the artificial enhancement of chemical weathering of rocks to accelerate atmospheric CO2 capture—is now widely seen as a potentially promising carbon dioxide removal (CDR) strategy that could help to achieve U.S. climate goals. Grinding rocks to smaller particle size, which can help to facilitate more rapid and efficient CO2 removal, is the most energy-demanding and cost-intensive step in the ERW life cycle. As a result, accurate life cycle analysis of ERW requires regional constraints on the factors influencing the energetic and economic demands of feedstock grinding for ERW. Here, we perform a state-level geospatial analysis to quantify how carbon footprints, costs, and energy demands vary among regions of the coterminous U.S. in relation to particle size and regional electricity mix. We find that CO2 emissions from the grinding process are regionally variable but relatively small compared to the CDR potential of ERW, with national averages ranging between ~5–35 kgCO2 trock−1 for modal particle sizes between ~10–100 μm. The energy cost for feedstock grinding also varies regionally but is relatively small, with national average costs for grinding of roughly 0.95–5.81 $ trock−1 using grid mix power and 1.35–8.26 $ trock−1 (levelized) for solar PV for the same particle size range. Overall energy requirements for grinding are also modest, with the demand for grinding 1 Gt of feedstock representing less than 2% of annual national electricity supply. In addition, both cost and overall energy demand are projected to decline over time. These results suggest that incorporating feedstock grinding into ERW deployment at scale in the coterminous U.S. should generally have only modest impacts on lifecycle emissions, cost-effectiveness, and energy efficiency.

Sustainable energy sources are continually advocated as the globe strives to transition from non-renewable forms. Energy security risks associated with the gradual depletion of petroleum resources and the related climate change effects require remedies. Nations have enacted laws stipulating biofuel blending mandates to reverse these adverse effects. Using the Low Emissions Analysis Platform (LEAP) software, this study assessed the potential long-term impact on energy demand and CO2 emissions from implementing B10 (10% biodiesel and 90% conventional diesel blend) in Uganda’s petroleum sub-sector was assessed. Four scenarios were analysed from 2019 to 2050: baseline (100% fossil diesel), B10, B20 (20% biodiesel and 80% conventional diesel blend), and electric-powered transport, were analysed for 2019 to 2050. The analysis revealed that replacing fossil diesel with B10 reduces fossil-derived energy demand by 3.52% and projects an 8.38% reduction in CO2 emissions compared to the baseline scenario. The B20 and electric vehicle scenarios offer an even greater reduction. Specifically, the B20 scenario shows an 8.46% decrease, and the electric scenario shows a 9.98% decrease in fossil-derived energy demand. These reductions are attributed to the lower proportion of fossil fuels in biodiesel blends and the higher energy efficiency of electric vehicles. B10 use as a substitute fuel for diesel-powered vehicles is expected to mitigate transport sector emissions in Uganda with minimal impact on final energy demand. B20 and electrification scenarios with lower final energy demand and higher environmental impact reductions are more advantageous relative to the B10 and baseline scenarios. Future analyses should establish the optimal renewable fuel and vehicular technologies mix for a net zero scenario by 2050 for Uganda’s transport sector.

Road transport is one of the main causes of air pollution around the world. Nowadays, the notion of sustainable development is one of the major pillars for a healthy environment. However, faced with its major challenges, it is more than necessary and important to understand a set of elements which contribute to better understanding the level of planning for sustainable road transport. This study aims to estimate energy demand and greenhouse gas emissions, namely from road CO2 transport, for the city of Douala in Cameroon from 2010 to 2035. In order to achieve our objective, we used the Long range Energy Alternative Planning (LEAP) model. Based on the LEAP model, the estimates were evaluated based on three scenarios Business As Usual (BAU), Energy Optimization and Mitigation (EOM) and Sustainability Mobility (SM). The aim of this work was to identify suitable potential policies with a view to reducing energy consumption and emissions carbon dioxide (CO2) for road transport in Douala. The results tell us that the EOM and SM scenarios have advantages over the BAU scenario with the SM scenario being more optimistic than the other two scenarios. In addition, the SM scenario has a growth rate of 87.408 % in energy demand smaller than the other two scenarios BAU and EOM respectively 151.598 % and 132.073 %. We also note that the SM scenario contributed to a reduction of 60.661 kilo ton emissions of CO2 less than the BAU scenario. At present, with a view to reducing energy consumption and reducing carbon emissions, road vehicles in circulation in the city of Douala should emphasize structural optimization measures through the use of clean energy by promoting the use of biofuels in mass transport, eliminating old cars and finally considering improving energy efficiency through the implementation of technological development in the automobile sector. Policy makers must take this study into consideration in order to better integrate the notion of energy sustainable road transport.

‘Sufficiency’ has become a key term within sustainability science. The satisfaction of basic human needs for all and for multiple generations is only possible if coupled to the implementation of ecological ceilings that prevent wealthy groups from disproportionate production and consumption patterns and energy use. While much sufficiency research has focused on the poor and how to provide corresponding social floors (i.e. an adequate level of sufficiency), this conceptual paper theorises structural barriers to implementing ecological ceilings for production and consumption by considering research on elites. The argument is illustrated with examples from urban spaces that are increasingly structured to accommodate the preferences of economic elites. A better scholarly understanding of the structural barriers to sufficiency dissemination can be facilitated through a systematic account of the political, social and symbolic strategies deployed by elites to accumulate and legitimise power. Conversely, a consideration of sufficiency principles would also be beneficial for research on elites, which has so far focused on social inequality and sidelined the ecological crisis. The discussion highlights how research on sufficiency and elite consumption may interact to be mutually beneficial. A research agenda is proposed to understand economic elites as barriers to the dissemination of sufficiency principles. Policy relevance Economic elites have a disproportionate ecological footprint if planetary boundaries are taken seriously and respected in policymaking. In an ecologically constrained planet, elite lifestyles undermine the satisfaction of basic needs of most of the population and future generations. Nevertheless, elites count as role models. This critical literature review reveals the separate traditions of research on sufficiency and elites, the political, social and symbolic strategies that economic elites employ to justify their privileged status in times of climate emergency and social–ecological transformation. These strategies are presented as major structural barriers to the dissemination of sufficiency principles that policymakers must consider when promoting and realising alternative and sustainable production and consumption patterns. The enormity of this task is illustrated by the fact that few people currently support policy measures to regulate economic elites via, for example, caps on income and wealth.

With the increasing threat from global warming, renewable energy has been considered an alternative in several forums. Kenya is an emerging economy endowed with renewable energy resources due to its geographical location. This review aims to identify policies guiding the extraction, utilization, challenges, and projection of renewable energy. A systematic approach was used in the study by considering relevant journal articles and other gray documents such as Energy Acts and reports from international and national organizations on renewable energy. The findings show that the energy matrix in Kenya comprises 80 % fossil fuels, 18 % renewable energy and 2 % coal. Over 85 % of the total population utilizes wood biomass, with 86 % in rural areas and 21 % in urban areas. Over 70 % of the Kenyan landmass is Arid and Semi-Arid, which have potential renewable energy. Electricity production in Kenya is produced by approximately 90 % renewable energy but has a target of 100 % transition by 2020. KenGen and IPPs generate 62.97 % and 35.95 % of the electricity generated, respectively. Geothermal energy in Kenya is the highest source of electricity at approximately 45 %. The current amount of electricity connected to the grid is 2708 MW in 2021, with projections of 4763 MW, 6638 MW and 9790 MW in the low, reference and vision scenarios, respectively. By 2022, the percentage of Kenyan who had access to electricity was 76.89 %. It is estimated that, by 2100, the population in Kenya will reach between 80 and 220 million according to projection scenarios. An increase in populations leads to a greater energy demand, which is implicated in climate change. Based on the RCPs, the energy demand outlook shows that RCP2.6 will increase by 2–3 times, while RCP8.5 will increase by 6–8 times the current energy demand. The challenges experienced by renewable energy include inadequate financing, implementation hindrances, inadequate infrastructure and technological challenges, outdated policies, overambitious projects, landscapes affecting transmission, corruption, high operational costs, and energy stacking affecting the process. Regular reviews on energy policies and innovations provide data that can be used to strategize future energy equity and access while maintaining environmental sustainability.

Wastewater treatment plants are essential in improving life quality by degrading organic matter, reducing contamination, and therefore greatly impacting human activities. The role of these critical treatment units can be further promoted by integrating new biological processes that currently are still under experimental scale. The co-digestion of sewage sludge and food waste has been proposed as an efficient way to increase plant treatment capacity and energy recovery. The assessment of hydrogen production along with food waste co-digestion is carried out in the present manuscript. Assessing several parameters is necessary to implement a new biological process in an operating plant, and quantifying its effects on the plant's overall performance is crucial. The implications associated with the extra equipment needed to handle additional waste material were evaluated. Results indicated that a conventional unit may treat a 10 % addition of food waste (expressed as VS) without experiencing severe modifications in process parameters, thus obtaining 16 % extra energy. However, the increase in food waste by over 10 % translates into substantial plant modifications requiring the installation of digesters with higher volumes and handling an additional amount of sludge. Another relevant factor is the lower energetic content of biogas when mixed with hydrogen. The increase in food waste until 50 % VS in the mixture reduced the biogas lower heating value to 15.5 MJ/m3. Future research will deal with an economic analysis of the approach and the effect on engine performance when dealing with a fuel mixture with different combustion properties.

The fast growth of the population is leading to an ever-increasing trend in energy consumption these days. In this regard, the construction industry is among the biggest consumers. Considering that most buildings are residential, energy optimization is essential, especially during the initial design phase. A practical way of building design with less energy demand is passive design methods. The importance of this issue is more visible in residential buildings in cold climates, which have the greatest temperature fluctuations. This study aims to investigate the energy demand of different passive strategies applied to a residential building in the cold climate of Tabriz, Iran and to select the most efficient design factor. The methodology is a combination of qualitative and quantitative methods. As the first step, considering the theoretical framework of the research, passive systems and the factors affecting building envelope thermal performance are determined. In the next step, EnergyPlus is used to analyze the application of the passive systems in the baseline model. All states are simulated separately in terms of the amount of heating and cooling energy demand and the results are presented in compareable graphs. In the third step, the strategy that has the greatest impact on reducing energy demand in cold-climate buildings is identified, and the most efficient alternative is presented through the analysis of different scenarios compared to the baseline model. This research reveals that heat loss through the envelope accounts for most of the energy demand, and thermal insulation plays an important role in reducing that loss. Also, different scenarios (materials, thickness and location) of thermal insulation were investigated. It shows that the optimal mode of thermal insulation in residential buildings in cold climates is to use polyurethane insulation with a thickness of 7 cm in the outer part of the building wall.

The residential sectorʼs substantial electricity consumption, driven by heating demands during winter, necessitates optimal energy consumption strategies in the era of decarbonization. To address this challenge, this paper introduces a synthetic dataset specifically tailored to simulate energy consumption in residential apartment buildings. Focusing on the interplay of cold weather conditions and the effects of aging factors, the dataset comprehensively encompasses key variables, including indoor temperature, energy consumption, outdoor temperature, outdoor humidity and solar radiation. It underscores the considerable impact of building aging on energy consumption patterns. The datasetʼs significance extends across various domains, particularly in the realms of energy forecasting and thermal modelling. It serves as a robust foundation for predicting future consumption patterns, optimizing resource allocation, and refining energy efficiency strategies. The inclusion of indoor temperature data facilitates an in-depth thermal modelling approach, shedding light on intricate relationships that influence building performance in cold climates. Beyond traditional, the dataset proves invaluable in nonlinear modelling and machine learning. It emerges as a key tool for algorithm training, enhancing forecast precision, and supporting well-informed decision-making. The introduction of a temporal dimension by accounting for aging factors allows for the exploration of evolving building components over time, a critical consideration for sustainable energy management and building maintenance strategies. The dataset was meticulously generated by creating geometry using SketchUp and conducting energy modelling and simulations via the OpenStudio platform, which integrates the Energy Plus modelling engine to enhance accuracy. In summary, this synthetic dataset generation provides valuable insights into energy consumption in residential buildings exposed to cold weather conditions and the influences of aging. Its multifaceted applications across forecasting, modelling, management, and planning underscore its potential to advance sustainable and efficient energy practices.

This study examines the complex and time-varying relationship between residential energy demand (including electricity, geothermal, and solar energy) and climate change using wavelet analyses with monthly USA data from January 1990 to March 2023. The results show that residential energy demand and climate change indicators exhibit a time-varying interrelationship with cyclical and lag effects. Specifically, before 2021, a positive correlation between residential electricity demand and carbon dioxide (CO2) emissions in short-term frequencies was found, but the relationship reversed thereafter, with an increase in CO2 levels influencing and decreasing residential electricity demand. In the long run frequencies, the link between residential power consumption and CO2 emissions shifted over time, exhibiting inconsistent co-movement. The co-movements between residential geothermal and CO2 show predominantly positive correlations, with CO2 leading the relationship in the short run, while geothermal leads the co-movements in the long run. In both short and long-term frequencies, the dependency and co-movement between residential solar and CO2 are mixed, with residential solar leading to positive correlations and CO2 leading to negative correlations. Therefore, improved insulation, energy-efficient windows, and high-efficiency heating systems can all assist in reducing heat loss and the total energy demand for domestic heating and subsequently low CO2 emissions.

The electricity demand is highly stochastic and unpredictable. This is due to the fact that it is significantly impacted by a number of factors, including the type of load, weather, time of day, seasonality, economic limitations, customers’ way of living, and other randomness factors. An accurate load model is one of the main inputs for the design of an economical and reliable renewable-based rural electrification system for rural communities and demand management systems. This paper presents a generic methodology for determining a rural community's energy consumption load profile, which is used to determine the most cost-effective size of renewable energy sources for rural electrification purposes. To determine the load profile parameters, such as the types of appliances used, their functioning times, functioning windows, and expected minimum and maximum cycle time, a field survey was conducted in four rural electrified Ethiopian villages. Since the survey findings will not fully explain the stochastic nature of the load profile, the load parameters are randomly generated, and a bottom-up approach is used to estimate the rural community's energy usage. In this study, household loads, public institution loads, business loads, and small industrial loads are the main consumer types taken into account. These loads are classified according to their energy usage as weekdays, weekends, and national and religious holidays. A MATLAB program is developed and implemented to obtain the load profiles of different customer groups. The results of this study are assessed in accordance with the multi-tier criterion and verified through the use of the well-known software HOMER Pro- and LoadProGen.

In December 2019, the World Health Organization announced the COVID-19 pandemic. This disease seriously endangers human life around the world and unfortunately, increases the number of infected and passed away people every day. Incidents of this magnitude have a major impact on the economy, energy consumption, and even the environment in a country. Depending on the policy pursued in each country to deal with COVID-19, the impact of this disease on energy consumption varies. In this paper, the electricity consumption situation is investigated in Iran during the pandemic period by using credible online data to articulate the decrease or increase in electricity consumption. It was observed that the electricity consumption in a period of fourteen months of the COVID-19 outbreak in comparison to before the pandemic time for residential, industrial, agricultural, commercial, public utilities and street lighting sectors were variations as 6.8 %, 22.4 %, 7.1 %, 6.1 %, −1%, and −0.3 % respectively. Due to the increase in overall electricity demand in Iran, to confront such problems it is proposed to strengthen the infrastructures of the energy sector by developing a comprehensive energy plan using intelligent systems, provision of appropriate policy and allocating more portion to renewable energy.

Abstract This study examines the short- and long-term impacts of various factors on the volatility and price of electricity in Cameroon, including hydroelectric power generation, economic growth, energy demand, and exchange rates from 2000 to 2019. The study uses an autoregressive distributed lag model. The study found that increasing hydroelectric power generation has both positive and negative impacts on electricity prices in the short and long term. While increasing the share of hydropower in overall energy production results in increased variation of electricity costs in the short term, it leads to significant price reductions in the long run. The study also found that economic growth has a considerable positive impact on the variation of power prices, while energy demand has a negative but insignificant effect on price volatility in the short term. Further, the study indicates that measures, such as encouraging SME engagement in renewable energy production, could improve the participation of local enterprises in the power industry and reduce the volatility of electricity prices. On the other hand, the study suggests that exchange rates could have a negative impact on electricity prices in the short term, but depreciation of the local currency could lower fuel costs and improve the availability of power. Overall, the study provides insights that can inform policymakers, energy regulators, and investors in making decisions that contribute to the efficient and sustainable development of Cameroon's electricity market. The study also highlights the need to prioritize power generation to stimulate economic growth and private investment while promoting renewable energy production.

Abstract Hybrid Centralised‐Decentralised Electric Vehicle (EV) coordination policy in the urgent charging scenario is presented. First, a robust and complex optimisation problem considering several key features affecting EV coordination is formulated. Then, a solution strategy for the formulated problem is proposed by decomposing the formulated problem into an EV coordination and simple optimisation problem. The decentralised rule‐based EV coordination strategy works on the principle of direct load flattening and utilises practical EV aggregator‐customer interaction, customer behaviour, and temporospatial shifting of the EVs to flatten the load duration curve at the charging station. Then, the centralised optimisation problem is solved to minimise the operation cost, decrease the power loss, and decrease congestion in the grid. A comparison between uncoordinated and coordinated charging in the case study conducted on the IEEE 24 bus system shows that the proposed approach reduces the average EV load by 1283.26 kW/min, average power loss by 2.465 kW/min, and operation cost by 61.99 $/min during the peak hours.

Rising energy costs have generated increasing interest in green networks among telecommunication operators. Purchasing or generating green energy for telecom networks can enhance energy sustainability and cost efficiency. However, the telecom industry, especially in Indonesia, has been slow to evolve owing to limited tools for telecom operators to conduct comprehensive studies and no successful business models to emulate when considering green power generation for their networks. In this study, we developed a macro-level energy demand assessment tool based on bottom-up information from the base transceiver station (BTS) to capture actual conditions as a preliminary stage for implementing a hybrid renewable energy system (HRES). We adopted energy assessment methods from the building sector and defined archetypes based on the BTS system configuration, type, and spatial information. Additionally, we used the hourly power consumption and traffic monitoring data to develop models for predicting the BTS power consumption and calculating the regional network energy demand. Our model quantifies the energy demand for the BTS network in each district of the Bali region, which accounts for 3.8% of 2022 total electricity consumption. With realistic energy demand information, we can create an HRES design that neither overestimates the energy demand, making the process too expensive, nor underestimates it. In conclusion, this study performs a macro-level analysis of energy demand calculations that consider the local characteristics of the BTS network and can be a reference for telecom operators as decision makers in implementing the HRES.

Considering the global increase in mean air temperature and society’s demand for a comfortable indoor microclimate, cooling solutions in buildings will play an essential role in achieving global climate goals. Researchers estimate that global energy consumption will be significantly affected by the energy required for cooling. Assessment of cooling demand potential is an essential step to further analyse cooling options for buildings in certain areas. Within the framework of this study, mapping of energy consumption for cooling in the city of Riga (Latvia) is carried out. Mapping allows to assess the demand in a spatial perspective, identifying areas with a denser energy demand indicator, and, accordingly, a higher potential for implementation of district cooling and this method can be used in other countries and cities as well. Energy consumption for cooling at the building level was calculated and visualized using cadastral data and building energy certificate data. The results show that the potential energy consumption for cooling in the city of Riga can reach up to 397 GWh per year and the largest proportion of energy consumption for cooling is predicted in the category of multi – apartment buildings (up to 155 GWh per year), highlighting the need to pay particular attention how cooling demand is addressed in residential building sector. Results can be further used to evaluate the technical and economic feasibility of the district cooling implementation in parts of the city.

This study aims to develop models for predicting hourly energy demand in the State of Connecticut, USA from 2011 to 2021 using machine learning algorithms inputted with airport weather stations’ data from the Automated Surface Observing System (ASOS), demand data from ISO New England (ISO-NE). We built and evaluated nine different model experiments for each machine learning algorithm for each hour of the day addressing energy demand patterns, variations between workdays and weekends, and COVID-19 impacts. Error metrics analysis results highlighted that the GBR model demonstrated better performance compared to the MPR and RFR models. Incorporating both temporal and weather features in the models resulted in a noticeable improvement in error metrics. A consistent overestimation trend was observed for all models during the validation period (2018–2019) which may be attributed to energy efficiency measures and integration of behind-the-meter generation, with a further notable increase in overestimation following the onset of COVID-19 due to a change of habits during the pandemic in addition to decarbonization initiatives in the State. This study emphasizes the need for adapting models to dynamic consumption and weather patterns for improved grid management.

With the intensification of global climate change, low-carbon energy has become a hot topic, and governments around the world are implementing corresponding policies to promote its use. This research first establishes a Multi-universe Quantum Harmony Search-Algorithm Dynamic Fuzzy System Ensemble (MUQHS-DMFSE) composite model for carbon emission prediction. This model combines the MUQHS algorithm with the DMFSE method by designing the workflow of the MUQHS algorithm, creating a DMFSE composite prediction model, introducing a sliding factor matrix, and using the MUQHS algorithm to search for the optimal sliding factors, thus obtaining optimized prediction values. In the research on low-carbon economic development, the research applies the Data Envelopment Analysis (DEA) method and establishes Charnes-Cooper-Rhodes (CCR) and Banker-Charnes-Cooper (BCC) models to assess the technical efficiency, pure technical efficiency, and scale efficiency of decision-making units. This research also uses the BCC model to project the production frontier and calculate input redundancy and output gap rates, and evaluate low-carbon economic development. Through the establishment and application of these two models, the research achieves carbon emission prediction and low-carbon economic analysis, validating the feasibility of the research methodology. The results show that the composite model can effectively predict carbon emissions, with a Mean Absolute Percentage Error (MAPE) below 3.5% and Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) below 200 tons, demonstrating the feasibility and accuracy of the model. The research on low-carbon economic development in S Province based on the DEA method reveals the need for energy structure adjustment, clean and renewable energy promotion, control of carbon emissions, and optimization of industrial structure with a focus on developing the tertiary industry. Therefore, the use of artificial intelligence and big data analysis can provide more precise insights into the trends and patterns of low-carbon economic development, as well as more effective predictions of future energy demand and resource supply, offering high practical value and scientific significance.

Monitoring energy consumption in response to rising temperatures has become extremely important in all regions of the globe. The energy required for cooling is a major challenge in West Africa, where the climate is predominantly tropical. Among the various methods for evaluating energy requirements, the degree-day method is best known for its ability to estimate the heating, ventilation, and air-conditioning (HVAC) requirements of buildings. This study used three decades of weather station data to assess the cooling degree days (CDD) in two major West African cities, Kano and Bamako, across a range of base temperatures from 22 °C to 30 °C. The results indicate an increase in cooling degree days for Kano, whereas Bamako experienced a decrease in these parameters over the same period. Nonetheless, Bamako required a relatively higher cooling demand for all base temperatures. Furthermore, the study showed that the years 1998 and 2015 had the most significant impact on Kano and Bamako, with CDD values ranging from 2220 °C-day to 218 °C-day for Kano and from 2425 °C-day to 276 °C-day for Bamako. The study also found that a lower base temperature leads to higher energy consumption, while a higher base temperature leads to lower energy consumption. This information provides a useful reference for governments and policymakers to achieve energy efficiency and reduce greenhouse gas emissions.

This paper demonstrates how artificial intelligence can be implemented in order to predict the energy needs of daily households using both multilinear regression (MLR) and single linear regression (SLR) methods. As a basic implementation, the SLR makes use of one input variable, which is the total amount of energy generated as an input. The MLR implementation involves multiple input variables being taken from various energy sources, including gas, coal, geothermal, wind, water, biomass, oil, etc. All of these variables are derived from detailed energy production data from the various energy sources. The purpose of this paper is to demonstrate that it is possible to analyze energy demand and supply directly together as a way to produce a more in-depth analysis. By analyzing energy production data from previous periods of time, a prediction of energy demand can be made. Compared to the SLR implementation, the MLR implementation is found to perform better because it is able to achieve a smaller error value. Furthermore, the forecasting pattern is carried out sequentially based on a periodic pattern, so this paper calls this method the recurrence multilinear regression (RMLR) method. This paper also creates a pre-clustering using the K-Means algorithm before the energy prediction to improve accuracy. Other models such as exponential GPR, sequential XGBoost, and seq2seq LSTM are used for comparison. The prediction results are evaluated by calculating the MAE, RMSE, MAPE, MAPA, and time execution for all models. The simulation results show that the fastest and best model that obtains the smallest error (3.4%) is the RMLR clustered using a weekly pattern period.

The Avoid–Shift–Improve framework has been used since its conception in the 1990s to help decision-makers prioritize action towards environmental sustainability in the transport sector. The core of the framework establishes a clear priority of action among the three main strategies that give it its name, thus highlighting the prominent role transport demand reduction should have within policy discussions. However, although its general formulation allows for a fruitful application to other sectors, the approach and its definitions have rarely been extended beyond transport. In particular, the energy sector could significantly benefit from an application of its methodology since the prioritization of energy demand reduction over energy efficiency would be in line with an optimized path towards decarbonization. This paper outlines a theoretical application of the A-S-I framework to the energy sector that allows the categorization of energy policies in terms of Avoid, Shift, or Improve strategies. Moreover, the paper presents an analysis of several energy policies databases to evaluate to what extent policies are addressing energy demand reduction, shift to less-carbon-intensive energy sources or energy efficiency. The results of the study show that most energy-related policies seem to support improving efficiency in current technology (18–33% of policies, depending on the database that is considered) and shifting towards low-carbon sources (28–48% of policies) more than reducing or altogether avoiding energy demand in the first place (6–22% of policies). Further research is recommended to strengthen the results, especially by evaluating the significance of each policy in terms of committed investment, as well as to understand the main factors that contribute to Avoid-type policies being under-represented in the energy sector.

Energy consumption demand has shown successive records during recent months, primarily associated with heat waves in almost all Brazilian states. The effects of climate change induced by global warming and the increasingly frequent occurrence of extreme events, mainly regarding temperature and precipitation, are associated with this increase in demand. In this sense, the impact of meteorological variables on load demand in some substations in the northeast and southeast of Brazil was analyzed, considering the historical series of energy injected into these substations. Fifteen substations were analyzed: three in the state of São Paulo, six in Bahia, three in Pernambuco, and three in Rio Grande do Norte. Initially, essential quality control was carried out on the energy injection data. The SAMeT data sets were used for the variable temperature, and Xavier was used for precipitation and relative humidity to obtain a homogeneous data series. Daily and monthly data were used for a detailed analysis of these variables in energy demand over the northeast and southeast regions of Brazil. Some regions were observed to be sensitive to the maximum temperature variable, while others were sensitive to the average temperature. On the other hand, few cases showed the highest correlation with the precipitation and relative humidity variables, with most cases being considered slight or close to zero. A more refined analysis was based on the type of consumers associated with each substation. These results showed that where consumption is more residential, the highest correlations were associated with maximum temperature in most stations in the northeast and average temperature in the southeast. In regions where consumption is primarily rural, the correlation observed with precipitation and relative humidity was the highest despite being negative. A more detailed analysis shows that rural production is associated with irrigation in these substations, which partly explains consumption, as when rainfall occurs, the demand for irrigation decreases, and thus energy consumption is reduced.

This study analyses data from natural gas combination boilers across a 6-year timeframe, exploring how demand for space heating and hot water have both changed over time, and highlights the impact of factors such as external temperature and the UK energy price cap. The results show that there has been a significant decrease in annual space heating and hot water demand since 2021. Space heating typically contributes 88% of the total annual gas demand of the boilers, with hot water contributing the other 12%. For the same mean temperature across the fourth quarter (8.5 °C), 2018 had a daily mean energy demand of 50.4 kWh, whereas the 2022 value was 41.4 kWh. This 9.0 kWh (18%) difference of the daily mean for Q4 suggests a shift in consumer demand influenced by other factors such as the energy price cap. This analysis provides additional understanding of how consumer energy demand for heating continues to evolve and invites further studies to be completed on future trends of energy demand for both space heating and hot water. Here, we also highlight the benefit of considering space heating and hot water as separate demands, as this provides additional insights and is something the paper helps to advocate for.

The lockdowns introduced to prevent the spread of COVID-19 had huge impacts, as people were largely confined to their homes. It could be expected that residential energy use would increase while non-residential decreased, however the picture is not so clear. Here three complementary datasets on different scales are used to explore changes in building energy use during two UK lockdowns: the complete building stock of Great Britain, a sample of approximately 1000 residential buildings, and one of about 24,000 residential boilers. Energy-signature analysis was used for the building data to estimate the changes in demand for space heating and other uses, with the boiler data able to separate space and water heating and explore changes in these. In the 2020 lockdown residential energy consumption for water heating and appliances increased, with decreased use for space heating, resulting in a reduction in total energy use during the heating season. In the 2021 lockdown total energy consumption changed little, however a decrease in the use of gas space heating was observed. The residential changes counteracted non-domestic changes, resulting in little difference in national energy consumption. These results highlight how longitudinal datasets enabled by Internet of Things-enabled devices can be crucial as an evidence base for research. Policy relevance The impacts on national energy consumption of increased time spent at home provide an important context for the pursuit of net zero targets, particularly with the rise in flexible working seen since 2020. That little change was seen overall could be due to the energy use in non-domestic buildings not decreasing proportionately to their decrease in use during national lockdowns, and thus seeking to lower non-domestic energy use to account for lower utilisation of spaces could deliver carbon and cost savings. This could be achieved through measures such as automated switch-off services, demand controlled heating and ventilation, and reduction in space. The finding that drastic changes were not made to home heating behaviours could have implications for future demand-side response schemes, indicating that perhaps occupants are happy to adapt to cooler conditions by achieving comfort in other ways.

Energy-outlook from past to future specific years has become essential in energy-economy. Malaysia is a member of ASEAN (Association of South-east Asian Nations), and ASEAN is increasing the use of renewable energy by 2050 to reduce carbon dioxide (CO2) emissions. Coal is Malaysia's primary fossil fuel for energy generation, producing large amounts of CO2 and exacerbating greenhouse problems, including air pollution. Moreover, renewable energy capacity has increased significantly worldwide since 2020 during the COVID-19 pandemic. Therefore, this study provides an energy-outlook study from 1990 to 2050 as a rationale for Malaysia's emphasis on sustainable energy in the post-COVID-19 years, which has been felt to be absent in previous studies. Here, past data as well as projections of future coal trade, hydropower growth, electricity consumption, renewable energy consumption, and CO2 emissions are shown. An energy-outlook procedure is explored by reviewing the literature to derive data visualizations and projections. Business-as-usual (BAU) and Alternative-policy Scenarios (APS) methods have been used for projections. The article prioritizes coal since coal is the main fossil fuel for electricity generation in Malaysia. The study observed a significant increase in CO2 emissions with increasing energy demand in Malaysia, which is a concern, and the article emphasized the importance of renewable energy in mitigating CO2 emissions. Finally, Long-short-term memory (LSTM) has been used as an initial experiment on how Artificial Intelligence (AI) can be used in the energy outlook study, which indicates the research scope of AI.

Household energy consumption (HEC) varies across neighbourhoods and gender groups. This database provides raw and analysed data on HEC determinants and their estimated influence on HEC in 2707 residential neighbourhoods (Wijk) in the Netherlands in 2018. The raw data consists of 17 indicators on energy demand, socioeconomic characteristics, microclimate and buildings. The indicators are retrieved from and calculated based on open national and international datasets. The analysed data presents the local coefficients of the HEC determinants, the outcome of the geographically weighted regression model (GWR) presented in the related article [1].

In a world grappling with escalating energy demand and pressing environmental concerns, microgrids have risen as a promising solution to bolster energy efficiency, alleviate costs, and mitigate carbon emissions. This article delves into the dynamic realm of microgrids, emphasizing their indispensable role in addressing today's energy needs while navigating the hazards of pollution. Microgrid operations are intricately shaped by a web of constraints, categorized into two essential domains: those inherent to the microgrid itself and those dictated by the external environment. These constraints, stemming from component limitations, environmental factors, and grid connections, exert substantial influence over the microgrid's operational capabilities. Of particular significance is the three-tiered control framework, encompassing primary, secondary, and energy management controls. This framework guarantees the microgrid's optimal function, regulating power quality, frequency, and voltage within predefined parameters. Central to these operations is the energy management control, the third tier, which warrants in-depth exploration. This facet unveils the art of fine-tuning parameters within the microgrid's components, seamlessly connecting them with their surroundings to streamline energy flow and safeguard uninterrupted operation. In essence, this article scrutinizes the intricate interplay between microgrid constraints and energy management parameters, illuminating how the nuanced adjustment of these parameters is instrumental in achieving the dual objectives of cost reduction and Carbon Dioxide emission minimization, thereby shaping a more sustainable and eco-conscious energy landscape. This study investigates microgrid dynamics, focusing on the nuanced interplay between constraints and energy management for cost reduction and Carbon Dioxide minimization. We employ a three-tiered control framework—primary, secondary, and energy management controls—to regulate microgrid function, exploring fine-tuned parameter adjustments for optimal performance.

The growing demand for renewable energy systems is driven by climate change concerns, government support, technological advancements, economic viability, and energy security. These factors combine to create a strong momentum towards a clean and sustainable energy future. Governments, governments, and individuals are increasingly aware of the environmental impacts of traditional energy sources and adopting renewable energy solutions. Hybrid Renewable Energy Systems (HRES) are developed as an effective way of meeting the energy demands in remote locations. The complexity of the system components and the fluctuation of renewable energy sources make it difficult to design an economical and effective HRES. In this study, the Improved Aquila Optimization (IAO) approach has been suggested as a powerful tool to optimize the HRES design. The study addresses the implementation of the IAO approach in the design of HRES and emphasizes its advantages over other optimization techniques. Through extensive simulations and analyses, our findings demonstrate the superior performance of the IAO algorithm in improving the efficiency and cost-effectiveness of HRES. The optimization process using IAO resulted in a significant reduction in overall system costs, achieving an estimated Net Present Cost (NPC) of $201,973. It translates to a cost reduction of 25% compared to conventional optimization techniques. Furthermore, our analysis reveals that the IAO approach enhances the utilization of renewable energy sources, leading to a 15% increase in overall energy generation efficiency. These results highlight the effectiveness of the IAO approach in addressing the challenges associated with designing HRES. By significantly reducing costs and improving efficiency, it facilitates the adoption of sustainable energy systems in remote areas. The outcomes of this study emphasize the importance of utilizing advanced optimization techniques, such as IAO, to ensure the economic viability and environmental sustainability of HRES.

This paper is centered on the prediction of electricity demand within the Southern Integrated Grid (S.I.G) for the forthcoming six years (2022–2027) through the utilization of advanced forecasting techniques. These methodologies encompass modified exponential, probabilistic or stochastic extrapolation and linear regression models. The foundation of these forecasts is established on a comprehensive examination of load curve data spanning 12 years (2010–2021) and an analysis of the factors influencing electricity demand in Cameroon. The models are implemented using Matlab and Excel, enabling the projection of results. The results of this analysis reveal a consistent growth trend in Cameroon's electricity demand between 2022 and 2027. the modified exponential model foresees an increase from 991.16MW to 1189.25MW, the probabilistic model from 969.68MW to 1109.20MW, and the linear regression model from 853.46MW to 975.89MW. Despite the inherent limitations, the outcomes prove to be satisfactory, marked by a high level of accuracy and reliability, as evidenced by mean absolute percentage error (MAPE) values of 0.2% for the modified exponential and probabilistic methods and 0.55% for the linear regression method. These scores fall well below the industry-accepted benchmark of MAPE < 10% outlined by the Lewis benchmark. In a comparative analysis, it is evident that the modified exponential and probabilistic models exhibit superior accuracy.

Abstract The present case study considers fuel base substitution in operation of actual district heating system and in other scenario replacing of district heating system by individual heating system in each apartment building and non-residential building in selected residential zone Zvolen-Sekier, Slovakia. The impact of each heating system was assessed with focus on ambient air quality based on air dispersion modelling of NO2 and CO pollutans using the AERMOD dispersion model. To identify the exposure level on residents, the magnitude and duration of exposure to the hazard were considered according to human health risk assessment method. Results showed that the individual heating systems released significantly higher NO2 and CO concentrations directly in the residential zone compared to district heating system. The obtained results were highly variable for individual scenarios and averaged periods of pollutants concentration. Investigated heating systems scenarios showed low (

Energy demand prediction can benefit electricity consumers, distribution network service providers, and system operators. It heavily depends on auxiliary factors, such as weather parameters (e.g., ambient temperature), which makes this problem more complex. Moreover, for many industrial applications, instead of aiming for the highest prediction accuracy, a more easily understandable and interpretable model that can lead to higher accuracy against the baseline model is the priority. Therefore, this problem still requires more investigation, especially when there is a specified prediction baseline to be compared with. This paper proposes a machine learning (ML) based prediction framework that investigates how temperature combined with energy consumption and simple and interpretable ML methods can be used to provide more precise demand forecasts and thus baselines closer to actual load profiles. The proposed framework is tested on two different real-world energy demand datasets. The analysis shows that using a simple ML model, such as a polynomial regression model, results in a more accurate prediction than the current baselines used in the energy market. The proposed ML models are not black-box type models, and thus are easier to explain and interpret. The ML-based forecasted demand is used as a baseline for demand response (DR) and is compared with the existing baselines used in the demand response market of Australia’s national grid.

Thailand’s policies are in accord with the global drive to electrify transportation vehicle fleets due to climate concerns. This dedication is evident through its adoption of the 30@30 initiative and the planned ban on new internal combustion (IC) engine vehicles by 2035, showcasing a strong commitment. The objective of this study was to utilize the Low Emission Analysis Platform (LEAP) software to model the transition possibilities for electric vehicle (EV). Emphasis was placed on the future of the light-duty vehicle (LDV) sector, encompassing the energy sources, electric power demands, and greenhouse gas (GHG) emissions. Two scenarios were evaluated: one involving rapid economic growth and the other characterized by a more-gradual expansion. The former projection foresees 382 vehicles per thousand people by 2040, while the latter estimate envisions 338 vehicles. In the scenario of high growth, the vehicle stock could surge by 70% (27-million), whereas in the case of low growth, it might experience a 47% rise (23.3-million) compared to the base year (15.8 million). The increased adoption of EVs will lead to a decrease in energy demand owing to improved fuel efficiency. Nonetheless, even in the most-extreme EV scenarios, the proportion of electricity in the energy mix will remain below one-third. While GHG emissions will decrease, there is potential for even greater emission control through the enforcement of stricter emission standards. Significant EV adoption could potentially stress power grids, and the demand for charging might give rise to related challenges. The deployment of public fast charging infrastructure could provide a solution by evenly distributing the load across the day. In the most-rapid EV penetration scenario, a public charging program could cap the demand at 9300 MW, contrasting with the 21,000 MW demand for home charging. Therefore, a recommended approach involves devising an optimal strategy that considers EV adoption, a tariff structure with incentives, and the preparedness of the infrastructure.

Energy utilisation has recently become a highly sought-after commodity on a global scale. This situation is not limited to Nigeria, where the national grid’s supply of electricity has been severely inadequate. This has hampered the country’s ability to meet the mounting needs of its large population and expanding economy. Aside from the global challenges of rising energy costs and environmental disasters, a number of factors such as a lack of interest in indigenous building technologies and materials have contributed to the Nigerian construction industry’s slow pace of meeting energy demand and achieving energy efficiency. This study investigates the possibility of achieving energy conservation through innovative materials, methods, and technology to increase energy efficiency and minimise energy demand in Nigeria’s residential housing. The objective is to determine the variables that influence energy usage in residential house design, select methods and technologies to reduce energy demand, and assess the best materials and processes. A quantitative approach to data collection was used by distributing questionnaires to respondents in the Minna metropolis. A hundred and forty (140) question- naires were distributed, and 117 of them were returned. Secondary data were obtained from literature reviews, journal articles, and conference papers. According to the findings, the most energy efficient residential buildings would result from the appropriate use of innovative ma- terials, methods, and technology to reduce the energy demand of the building. In order to decrease energy demand, the study suggests that built environment professionals should focus largely on changing energy-consuming devices and their end uses for energy efficiency. It concluded that, in order to reduce overlap between the applications of these elements and bet- ter meet the needs of building occupants in terms of energy usage in Nigeria, energy demand should be considered from the design stage.

Despite low electricity access rates and weakened electricity sector governance under the pressure of population growth, modes of access to electricity in Lubumbashi vary spatially and in their typologies. However, to date, there is no detailed information on mapping households’ modes of access to the National Electricity Company (SNEL) electricity network. This study aims to assess and map both formal and informal methods of household access to electricity in Lubumbashi. A socio-technical survey was conducted among 1211 households in the 7 communes or districts of the city. The results showed that the electricity access rate in the different districts of Lubumbashi varies between 41.6 and 78.3%, with an urban average of 63.8 %. The lowest rate is observed in the Annex district, while the highest is kept in the Kamalondo commune. At both the rural and urban levels, the most dominant method of access to electricity is a connection to the grid (formal and legal methods), with the highest rate in the Kamalondo, Kenya and Lubumbashi districts. Clandestine electrical connections are most common in the communities of Kamalondo and Kenya. The Annexe and Lubumbashi districts have the lowest rates of illegal electric connections. This study showed that the spatial distribution of cases of electricity theft and non-payment of bills in Lubumbashi is influenced by complex factors, such as the interaction between urban growth, household income level and the governance mode. The fight against fraudulent connections should be based on the present study to propose localized and adequate solutions in the different districts.

Covid-19 pandemic and the restrictions imposed to control its spreading had a profound impact on several aspects of society. Studies on energy consumption show a pattern shift during the first stages of the pandemic, mainly due to the abrupt and forced change in human behaviour. For some residential consumers, such changes translated into higher energy expenses, which could be avoided through optimal energy management. This, however, depends on accurate load forecasting models whose performance tends to decrease under drastic pattern changes. This paper aims to improve forecasting techniques based on machine learning methods to assess its impact on the accuracy of day-ahead electrical load forecasting from individual residential households from the city of Barcelona, Spain, during a lockdown situation. Support Vector Machine (SVM) is the proposed algorithm for the short-term hourly forecasting of the households’ electricity consumption of the following 24 h. The algorithm’s accuracy over different pandemic periods – Lockdown, Reopening and New Normal – is evaluated using different retraining strategies based on literature findings.

With the increased market penetration of electric vehicles (EVs), energy consumption is growing at a quicker rate. Electric vehicles are slowly but surely gaining a foothold in India’s rapidly expanding market. With more EVs on the road, there will be more demand for charging and more energy use in some places, which can put a lot of stress on the power grid. In the absence of sufficient planning, there is a risk of power system interruption and failure. Therefore, the need for energy in cities will shift in light of the advent of EVs in the next decade or two. Accordingly, Surat has been chosen as the research region for the planning of charging infrastructure in the current study. After reviewing the literature, primary data was acquired to estimate citizen travel demands. Vehicle registration statistics were compiled from the Regional Transport Office (RTO) for the purpose of EV forecasting. After collecting data, the EV population and demand for public charging stations by 2025 and 2030 were calculated. This study aims to meet the public charging demand in the city of Surat by analyzing the electric vehicle growth rate and vehicle forecast. This anticipated data was then utilized to compute the energy demand consumption for Surat city by calculating vehicle kilometers traveled. The demand for public charging comes out to be 42.4 MWh/day in 2025 and 358.05 MWh/day in 2030, respectively. The study’s objective is to determine energy demand per region in order to better understand grid planning due to the imbalance in electricity demand.

Background: Repeated sprint ability (RSA) is defined as the ability to recover and maintain maximal effort during repeated sprints, recognised as a crucial performance component in team sports. The exercise mode used to test RSA may influence performance and the contributions of different energy systems. The primary aim of this study is to address the critical gap between traditional cycling-based anaerobic tests, such as the Wingate test, and the practical, sport-specific demands of running in field-based team sports. Methods: This study involved 32 professional soccer players (age: 21.2 ± 1.3 years; height: 177.8 ± 4.3 cm; and mass: 71.3 ± 6.4 kg). They performed cycling- and running-based repeated sprint tests, with similar total sprint numbers, durations, and recovery times, on different days. Contributions from adenosine triphosphate-phosphocreatine (ATP-PCr), glycolytic, and oxidative systems were estimated through body weight, oxygen uptake (VO2), blood lactate (BLa), and the fast component of excess post-exercise oxygen consumption (EPOC). The VO2 levels and heart rate (HR) were monitored during the rest (10 min), exercise, and recovery (15 min) phases in a breath-by-breath mode using a portable gas exchange system. BLa was measured before (at rest) and 1, 3, 5, 7, and 10 min after the running and cycling tests using a handheld portable analyser. A mono-exponential model estimated the ATP-PCr system contribution, calculated using the fast component of EPOC following the final sprint and the sum of the VO2-time integral during rest intervals. Results: The cycling tests demonstrated significantly higher values for the peak power (PP), mean power (MP), and rate of perceived exertion (RPE) (p < 0.05), while the heart rate peak and blood lactate responses were similar across all modalities. The fatigue index was notably higher in the running tests (p < 0.05). Furthermore, the running tests showed greater contributions in both the percentage and absolute terms from the adenosine triphosphate-phosphocreatine (ATP-PCr) system (p < 0.01), total energy demand (p < 0.05), and total energy expenditure (TEE) (p < 0.01). Notably, the running tests resulted in an increased phosphocreatine breakdown (p < 0.05) and rapid phosphocreatine replenishment (p < 0.01). A simple linear regression analysis highlighted a significant determination coefficient between these performance variables and the contributions of the energy systems, affirming the robustness of the results. The correlation heatmaps further illustrated these relationships, with higher correlations for the PP and MP across modalities (0.41), emphasising the moderate association between cycling and running tests in these metrics. Conclusions: This study elucidated the similarities and differences in energy system contributions and performance outcomes between a cycling and a running repeated sprint protocol, with a comparable total sprint time and work–rest ratio. The findings reveal that a running repeated sprint test elicits a higher energy demand and a higher contribution from the PCr energy system compared to cycling. Performance variables were not associated between running and cycling tests, suggesting those tests cannot be used interchangeably.

With the global pursuit of renewable energy and carbon neutrality, hydrogen-based microgrids have also become an important area of research, as ensuring proper design and operation is essential to achieve optimal performance from hybrid systems. This paper proposes a distributed control strategy based on multiagent self-triggered model predictive control (ST-MPC), with the aim of achieving demand-side control of hydrogen-based microgrid systems. This architecture considers a hybrid energy storage system with renewable energy as the main power source, supplemented by fuel cells based on electrolytic hydrogen. The primary objective of this architecture is aiming at the supply and demand balance problem under the supply and demand relationship of microgrid, the service life of hydrogen-based microgrid energy storage equipment can be increased on the basis of realizing demand-side control of hydrogen energy microgrid system. To accomplish this, model predictive controllers are implemented within a self-triggered framework that dynamically adjusts the counting period. The simulation results demonstrate that the ST-MPC architecture significantly reduces the frequency of control action changes while maintaining an acceptable level of set-point tracking. These findings highlight the viability of the proposed solution for microgrids equipped with multiple types of electrochemical storage, which contributes to improved sustainability and efficiency in renewable-based microgrid systems.

The harvesting of microalgae biomass cultivated during different processes is still identified as the main driver of biomass production cost. Particularly in the field of wastewater treatment, an energy-efficient and reliable harvesting or separation method is needed to remove microalgae biomass from the wastewater after nutrient assimilation. In this study, the suitability of pile cloth media filtration (PCMF) for microalgae harvesting during wastewater treatment is investigated. A mini plate PCMF was operated over 18 months with three different pile cloth media as part of a pilot-scale wastewater treatment plant incorporating a microalgae treatment step for phosphorus and nitrogen removal. During this time, the removal rates and achievable total suspended solids (TSS) concentration in the effluent were recorded. Differences between the three pile cloth media were noticeable, with TSS concentrations ranging from 9.7 mg·L−1 to 17.7 mg·L−1. The pilot-scale data were used to determine the dimensions of a large-scale PCMF and to estimate its energy demand. This resulted in theoretical energy demands of 7 to 8 Wh·m−3 or 37 Wh·kg−1 TSS, considerably lower than the energy demand of other harvesting technologies.

Nowadays, the methodology aiming to achieve carbon neutrality and net zero energy on a large scale is known. Despite this, few specialists are mastering this technology globally. What new scenarios. applied at the neighbourhood scale. generate a significant reduction in the rate of CO2 emissions and energy demand? In addition, a lack of massive, regular, and consistent data on carbon emissions and energy consumption has made it significantly difficult to understand the origins of climate change at the building and neighbourhood scales. This work has, as its main goal, the assessment of different strategies that facilitate reduction in the concentration of CO2 and lower energy demands at the district level. The life cycle assessment of a new district has been carried out over 100 years during the four stages of the life cycle of the neighbourhood (construction, operation, demolition and end of life). The results showed that up to 93% of greenhouse gas (GHG) was produced during the operational stage. The energy demand due to transport and waste management represented 60% of the total energy demand of the district during the operational stage. There is still a possibility to maintain air temperature growth around 1.5 °C in the next decade by means of the following: Global warming + 100% of heavy renovation of all buildings + 100% of electric car − renewable energy. This strategy would facilitate a reduction of over 92% of the CO2 produced at the district level.