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An alternative to the sugar/starch-based crops bioethanol is lignocellulosic biomass, but its utilization to biofuels is still not economically viable. In this context, an increasing interest has arising on the search for specific energy crops that do not require arable lands and are not water intensive, such as Jerusalem artichoke (JA). So, this work consisted on the cultivation of JA on those agricultural conditions and its further evaluation as a sustainable feedstock towards bioethanol. Two strategies of producing bioethanol were evaluated pointing out for the consolidated bioprocessing with the Zygosaccharomyces bailii Talf1 yeast as the best approach for further scale-up, based on energy data analysis and ethanol productivity. Different industrial scenarios were outlined and compared for overall CO2eq emissions and energy consumption per liter of ethanol (LEtOH), using adequate criteria on a cradle-to-gate approach. With no land-use change, no biogenic and no co-products credits, the comparison of the overall energy consumption and CO2eq emissions (100% process) from JA ethanol (9 MJ/LEtOH; 679 g CO2/LEtOH) with sugarcane/sugar beet ethanol (42/29 MJ/LEtOH; 731/735 g CO2/LEtOH) and with gasoline refinery (15 MJ/LEtOH eq; 1154 g CO2/LEtOH eq), highlights the JA as an alternative feedstock to be a focus of ethanol research for gasoline blends.

This article analyzes the potential for electric power generation and cost from commonly disposed Empty Fruit Bunches (EFB) from palm oil mills. To identify the best options, 40 plant configurations and two turbine configurations were simulated: Backpressure Turbine (BPT), Condensing-Extraction Turbine (CET); four types of EFB pretreatment: EFB Crude; EFB W/M; EFB D/M; EFB P; and five gas temperatures generated in the boiler (320 psig/320 °C; 320 psig/400 °C; 400 psig/450 °C; 600 psig/450 °C; 900 psig/500 °C). Results showed that configurations with the CET and steam at high pressure (>400 psig) with EFB type D/M produced the most energy. Generation with raw and pelletized EFB generated low amounts of energy due to vaporization heat requirements and energy consumption by peripheral equipment, especially with the BPT. CET configurations had the lowest levelized cost of energy (LCOE) at 390 COP/kWh, which is close to the average energy price around Colombian palm mills. Initial cost estimates showed that CET with high pressure steam at weighted energy price >230 COP/kWh are currently profitable (NPV > 0). Commercialization of energy obtained from low pressure steam is only calculated to be profitable when the price of electricity is > 320 COP/kWh.

Achieving further reductions in building electricity usage requires a detailed characterization of electricity consumption in homes. Understanding drivers of consumption can inform strategies for promoting conservation and efficiency. While there exist numerous approaches for modeling building energy demand, the use of regularization methods in statistical models can address challenges inherent to building energy modeling while also enabling more accurate predictions and better identification of variables that influence consumption. This paper applies five regularization techniques to regression models of original survey and electricity consumption data for more than one thousand households in California. It finds that of these, elastic net and two extensions of the lasso—group lasso and adaptive lasso—outperform other approaches in terms of prediction accuracy and model interpretability. These findings contribute to methodological approaches for modeling energy consumption in buildings as well as to our understanding of key drivers of consumption. The paper shows that while structural factors predominate in explaining annual electricity consumption patterns, habitual actions taken to save energy in the home are important for reducing consumption while pro-environmental attitudes and energy literacy are not. Implications for improving building energy modeling and for informing demand reduction strategies are discussed in the context of the low-carbon transition.

Energy efficiency, which is one of the pillars of the EU's Energy Union strategy, has been proposed as a solution, namely as a highly effective pathway to improve economic competitiveness and sustainability of the European economy, lower emissions, reduce energy dependency and increase security of supply, and job creation. The paper reviews the EU strategies and policies on energy efficiency and argues that further focus should be placed on industrial energy efficiency. Despite a decline in energy consumption in recent years in industry, this sector is one of the largest users of energy in the EU. Therefore, the paper reviews the extent to which the European and national policies in the selected jurisdictions, such as Italy and the UK address energy efficiency in industry and whether there are any measures in place to promote it. Horizon 2020 ETEKINA

‘Energy efficiency first’ is one of the key principles of the Energy Union, mainly due to it being the most cost effective way to reduce emissions, improving energy security, enhancing competitiveness and making energy consumption more affordable for all consumers. In light of the revised EU Energy Efficiency Directive, this paper discusses new developments brought by the EU together with the national case studies of Slovenia and Spain. Given that the paper has a specific focus on the industrial sector, it discusses the selected measures of the Energy Efficiency Directive, such as defined in Articles 7, 8, and 14, which are the most relevant to this sector. The paper also explores the newly issued integrated national energy and climate plans together with national measures and policies that support energy efficiency in industry, including the quantification of achieved and forecast energy savings in these two EU Member States., Highlights • The revised EU Energy Efficiency Directive is the key driver of change in Member States. • More ambitious energy efficiency targets are projected in Slovenia and Spain for 2030. • The integrated National Energy and Climate plans inspired further national developments. • Both countries committed to continue their national measures/schemes on energy efficiency.

This paper employs an LMDI decomposition exercise to investigate the main factors that affect changes in CO2 emissions of South Africa at national level within the BRICS group of countries (Brazil, Russia, India, China, and South Africa) from 1990 to 2014. From the results, it is expected to derive some information on the existence of the energy rebound hypothesis for South Africa with ultimate purpose to direct future research into examining the effect in a micro level. The aim of this paper is not to estimate the precise rebound effect but to decompose the determinants of emission changes in the BRICS countries. The concept of energy rebound would play a role if the improvements in energy intensity might lead to emission increases, instead of the opposite as expected. The overall results suggest that the changes in CO2 intensity and energy intensity had a negative impact to the changes in CO2 emissions: in other words, as the energy intensity (energy consumption per unit of economic output) decreased for all the countries (possible technological developments), the emissions kept rising. For South Africa specifically, the energy intensity was a negative contributor to CO2 emissions only for the part of the examined period (2008–2014).

The paper presents a possible approach for creating business decisions based on multi-criteria analysis. Seven options for a possible revitalization of the thermal power plant Kolubara-A Unit No. 2 with energy indicators of sustainable development (EISD) are presented in this paper. The chosen EISD numerically express the essential features of the analyzed options, while the sustainability criteria indicate the option quality within the limits of these indicators. In this paper, the criteria for assessing the sustainability options are defined based on several aspects: economic, social, environmental and technological. In the process of assessing the sustainability of the considered options the Analysis and Synthesis of Parameters under Information Deficiency (ASPID) method was used. In this paper, the EISD show that production and energy consumption are closely linked to economic, environmental and other indicators, such as economic and technological development of local communities with employment being one of the most important social parameter. Multi-criteria analysis for the case study of the TPP Kolubara-A clearly indicated recommendations to decision makers on the choice of the best available options in dependence on the energy policy. (C) 2017 Elsevier Ltd. All rights reserved.

This paper aims to complete and complement the information available on the past trends of thermal energy consumption in residential buildings and its drivers for Europe, as a continent and for the different countries. This paper follows the drivers identified in a previous one for the heating and cooling energy consumption, decomposing this energy demand into key drivers based on a Kaya identity approach: number of households, persons per household, floor space per capita and specific energy consumption for residential heating and cooling. Results show that all drivers did follow a consistent trend at global, regional and country level during the studied period of time, but the heating and cooling energy consumption did not follow the same trend if it was considered at global, regional or country level, showing that the energy consumption is very much influenced by all its drivers and does not follow the same trend as the specific energy consumption. Moreover, similar trends for each indicator can be found when evaluated at country or regional level, therefore as expected when aggregating data trends can be seen but details and particularities (both for the different countries and for the different studied years) are lost. The work is partially funded by the Spanish government (ENE2015-64117-C5-1-R (MINECO/FEDER)). The authors would like to thank the Catalan Government for the quality accreditation given to their research group GREA (2014 SGR 123) and research group DIOPMA (2014 SGR 1543). Dr. Camila Barreneche would like to thank Ministerio de Economia y Competitividad de España for Grant Juan de la Cierva, FJCI-2014-22886.

In this paper, an IAM (integrated assessment model) uses a newly-developed Monte Carlo analysis capability to analyze the impacts of more aggressive U.S. residential and commercial building-energy codes and equipment standards on energy consumption and energy service costs at the state level, explicitly recognizing uncertainty in technology effectiveness and cost, socioeconomics, presence or absence of carbon prices, and climate impacts on energy demand. The paper finds that aggressive building-energy codes and equipment standards are an effective, cost-saving way to reduce energy consumption in buildings and greenhouse gas emissions in U.S. states. This conclusion is robust to significant uncertainties in population, economic activity, climate, carbon prices, and technology performance and costs.

Space and water heating are two major components of household energy consumption, and a number of work has been done to combine multiple residential heating technologies to develop hybrid heating solutions that are more flexible, efficient and economical. This paper studied and modelled a smart home with a novel hybrid heating system, which consists of a wall-hung gas boiler (for both space and water heating) that is connected to a water tank with an auxiliary electric resistance heater (for water heating), and auxiliary split air conditioners (for space heating). A multi-objective optimization was further formulated to optimally schedule the gas and electricity consumption of a smart home equipped with the hybrid heating system as well as an onsite photovoltaic (PV) system, a battery energy storage system, an electric vehicle and various types of flexile loads. Two objectives, i.e. the minimization of operating energy cost (including both electricity and gas payment) and the maximization of thermal comfort, were considered. The optimization problem was finally transformed to a single objective mixed integer linear programming problem that can be solved by existing optimization solvers. Simulation results verified the operational performance of the studied hybrid heating system and the effectiveness of the proposed scheduling method. Sensitivity analysis was conducted considering various levels of electricity/gas prices, onsite PV capacity, battery capacity and outdoor air temperature, and it shows that in all the scenarios the operating energy cost of the smart home with the hybrid heating system was always lower than that with the pure electricity or pure gas heating systems. The performance under various uncertainties and the benefits of adopting model predictive control were also demonstrated. It is noted that this paper only focuses on the optimal scheduling and operation costs of the hybrid heating system. Optimal configuration and sizing of the heating system with the initial investment (i.e. capital expenditures) considered are identified as research directions in the future.

Statistical models can only be as good as the data put into them. Data about energy consumption continues to grow, particularly its non-technical aspects, but these variables are often interpreted differently among disciplines, datasets, and contexts. Selecting key variables and interactions is therefore an important step in achieving more accurate predictions, better interpretation, and identification of key subgroups for further analysis. This paper therefore makes two main contributions to the modeling and analysis of energy consumption of buildings. First, it introduces regularization, also known as penalized regression, for principled selection of variables and interactions. Second, this approach is demonstrated by application to a comprehensive dataset of energy consumption for commercial office and multifamily buildings in New York City. Using cross-validation, this paper finds that a newly-developed method, hierarchical group-lasso regularization, significantly outperforms ridge, lasso, elastic net and ordinary least squares approaches in terms of prediction accuracy; develops a parsimonious model for large New York City buildings; and identifies several interactions between technical and non-technical parameters for further analysis, policy development and targeting. This method is generalizable to other local contexts, and is likely to be useful for the modeling of other sectors of energy consumption as well.

Improvement of energy efficiency has been accepted as one of the most cost-effective approaches towards sustainable economic development and reduction of the continuously increasing energy consumption internationally. South Africa, being among the developing countries, is not an exception even though historically low energy prices and the lack of appropriate policies have created an energy intensive economy. This paper examines the factors affecting the trends in energy efficiency in South Africa from 1993 to 2006 and particularly the impact of structural changes and utilisation efficiency of the country's energy intensity. Identifying and understanding the driving forces are necessary ingredients in the development of appropriate policy-making. This paper also provides disaggregation of the energy efficiency trends in the main sectors of the economy. We determine that structural changes of the economy have played an important and negative role in the increasing economy-wide energy efficiency. On the other hand, the energy usage's intensity was a contributing factor to the decreasing trend of energy efficiency. We suggest that differentiated price policies may be required if South Africa is to create an effective energy efficiency policy.

This paper challenges the view that improving the efficiency of energy use will lead to a reduction in national energy consumption, and hence is an effective policy for reducing national CO 2 emissions. It argues that improving energy efficiency lowers the implicit price of energy and hence make its use more affordable, thus leading to greater use—an effect termed the ‘rebound’ or ‘takeback’ effect. The paper presents the views of economists, as well as green critics of ‘the gospel of efficiency’. The paper argues that a more effective CO 2 policy is to concentrate on shifting to non-fossil fuels, like renewables, subsidized through a carbon tax. Ultimately what is needed, to limit energy consumption, is energy sufficiency (or conservation) rather than energy efficiency.

The paper discusses the energy consumption and energy saving potential for a major energy-intensive product in the chemical industry-ammonia, based on technologies currently in use and possible process improvements. The paper consists of four parts. In the first part, mainly references to various ammonia production technologies are given. Energy consumption, emissions and saving potentials are discussed in the second part. Thereby, the situation in Europe, the US and India is highlighted and various data sources are compared. In the third part of the paper, a novel approach for modeling energy efficiency improvements is described that accounts for uncertainties and unobserved heterogeneity in the production processes. Besides new investments, revamping investments are also included in the modeling and the development of the production stock is accounted for. Finally, in the fourth part, this approach is applied to the modeling of energy efficiency improvements and CO2 emission reductions in ammonia production. Thereby, considerable improvements in specific energy use and CO2 emissions are found in the reference scenario, yet under the assumption of high oil and gas prices, a partial switch to coal based technologies is expected which lowers notably the CO2 efficiency. Introduction of a CO2 penalty under a certificate trading or other regime is on contrary found to foster energy efficiency and the use of low carbon technologies.

Reductions in lighting energy have secondary effects on cooling and heating energy consumption. In general, lighting energy reductions increase heating and decrease cooling requirements of a building. The net change in a building's annual energy requirements, however, is difficult to quantify and depends on the building characteristics, operating conditions, and climate. This paper characterizes the effects of lighting/HVAC interactions on the annual heating/cooling requirements of prototypical US commercial buildings through computer simulations using the DOE-2.1E building energy analysis program. Twelve building types of two vintages and five climates are chosen to represent the US commercial building stock. For each combination of building type, vintage, and climate, a prototypical building is simulated with varying lighting power densities, and the resultant changes in heating and cooling loads are recorded. These loads are used together with market information on the saturation of the different HVAC equipment in commercial buildings to determine the changes in energy use and expenditures for heating and cooling. Results are presented by building type for the US as a whole. Therefore, the data presented in this paper can be used to assess the secondary effects of lighting-related federal policies with widespread impacts, such as minimum efficiency standards. Generally, in warm climates the interactions will induce monetary savings and in cold climates the interactions will induce monetary penalties. For the commercial building stock in the US, a reduction in lighting energy that is well distributed geographically will induce neither significant savings nor significant penalties from associated changes in HVAC primary energy and energy expenditures.

This paper presents a conceptual framework for analyzing persistence of energy savings, summarizes the limited experience of what we know about persistence, provides guidance for conducting retrospective and prospective persistence studies, and suggests strategies for ensuring persistence. Because this area of research is in its infancy, unequivocal conclusions about persistence would be premature. Accordingly, this paper provides guidance for both conducting research in this area and developing policies and mechanisms to help ensure the persistence of energy savings.

This paper investigates the impact of flameless oxy-fuel combustion on the thermal performance of a pilot-scale steel reheating furnace. A comprehensive mathematical model, based on the zone method of radiation analysis, was developed, which takes into account the non-grey behaviour of the furnace atmosphere under oxy-fuel combustion conditions. The model was subsequently used to simulate the temperature profile of an instrumented slab used in the experiment. The results showed that the predicted slab temperature profile along the furnace is in good agreement with measurement. However the model over predicted the absolute slab temperatures due to the influence of formation of oxide scales on the slab surface, which was not taken into account in the current model. When compared to air-fuel combustion simulation, the results of oxy-fuel combustion also indicated a marked improvement in the furnace specific fuel consumption (approximately 16%). This was mainly due to the enhanced radiative properties of the furnace atmosphere and reduced exhaust energy losses as the result of less dilution effect from nitrogen. This resulted in reduction in the overall heating time by approximately 14 min. Furthermore, if the economics of carbon capture is taken into consideration, theoretically, the energy consumption per kilogram of CO2 captured can be reduced from 3.5 to 4.2 MJ kg−1 to 0.96 MJ kg−1. In conclusion, the current studies support the view that oxy-fuel combustion retrofitting to reheating furnaces is a promising option, both from a technical and from an environmental point of view.

Our world's future is strongly connected to energy consumption trends. There are bi-directional relations between energy consumption and the average temperature of Earth, leading to positive causal loops. Increasing temperatures cause activity of more cooling systems most of which are electrified by burning hydrocarbons that consequently yield more carbon dioxide concentration and warmer climates. This paper is a trial to estimate the loop gain by employing a bottom-up regional model. The model is a spreadsheet containing sets of parameters and variables to estimate electricity used for cooling buildings in the residential and commercial sectors of 12 regions all around the world. The share of fossil-fuel based power plants determines each region's contribution to CO2 emissions. Then, by processing data on the global emission trend and land temperature anomaly, a linear ARMAX relationship is estimated to compute the loop gain. The results show that, even in the optimistic scenario of IPCC (A1B), emission from cooling electricity will double up by the end of the century. With the estimated 1 + 1.4 × 10-6 loop gain, even if fossil-fuel electricity generation is gradually reduced to 40%, after a short fall, it will start growing again in the mid-century.

Future building energy management systems will have to be capable of adapting to variation in the rate of production of energy from renewable sources. Controllers employing a model predictive control (MPC) framework can optimise and schedule energy usage based on the availability of renewably generated energy. In this paper, an MPC using artificial neural networks (ANNs) was implemented in a residential building. The ANN-MPC was successfully tested and demonstrated good performance predicting the building's energy consumption. The controller was then modified to function as an economic MPC (EMPC) to optimise demand flexibility (i.e., the ability to adapt energy demands to fluctuations in supply). The operational costs of energy usage were associated with this demand flexibility, which was represented by three flexibility indicators: flexibility factor, supply cover factor, and load cover factor. The results from a day-long test showed that these flexibility indicators were maximised (flexibility factor ranged from −0.88 to 0.67, supply cover factor from 0.04 to 0.13, and load cover factor from 0.07 to 0.16) when the EMPC controller's demand flexibility was compared to that of a conventional proportional-integral (PI) controller. The EMPC framework for demand flexibility can be used to regulate on-site energy generation, grid consumption, and grid feed-in and can thus serve as a basis for overall optimisation of the operation of heating systems to achieve greater demand flexibility.

Finding suitable forecasting methods for an effective management of energy resources is of paramount importance for improving the efficiency in energy consumption and decreasing its impact on the environment. Natural gas is one of the main sources of electrical energy in Algeria and worldwide. To address this demand, this paper introduces a novel hybrid forecasting approach that resolves the two-stage method's deficiency, by designing a Multi Layered Perceptron (MLP) neural network as a nonlinear forecasting monitor. This model estimates the next day gas consumption profile and selects one of several local models to perform the forecast. The study focuses firstly on an analysis and clustering of natural gas daily consumption profiles, and secondly on building a comprehensive Long Short Term Memory (LSTM) recurrent models according to load behavior. The results are compared with four benchmark approaches: the MLP neural network approach, LSTM, seasonal time series with exogenous variables models and multiple linear regression models. Compared with these alternative approaches and their high dependence on historical loads, the proposed approach presents a new efficient functionality. It estimates the next day consumption profile, which leads to a significant improvement of the forecasting accuracy, especially for days with exceptional customers consumption behavior change.

When developing an energy management strategy (EMS) including a battery aging model for plug-in hybrid electric vehicles, the trade-off between the energy consumption cost (ECC) and the equivalent battery life loss cost (EBLLC) should be considered to minimize the total cost of both and improve the life cycle value. Unlike EMSs with a lower State of Charge (SOC) boundary value given in advance, this paper proposes a model predictive control of EMS based on an optimal battery depth of discharge (DOD) for a minimum sum of ECC and EBLLC. First, the optimal DOD is identified using Pontryagin's Minimum Principle and shooting method. Then a reference SOC is constructed with the optimal DOD, and a model predictive controller (MPC) in which the conflict between the ECC and EBLC is optimized in a moving horizon is implemented. The proposed EMS is examined by real-world driving cycles under different preview horizons, and the results indicate that MPCs with a battery aging model lower the total cost by 1.65%, 1.29% and 1.38%, respectively, for three preview horizons (5, 10 and 15 s) under a city bus route of about 70 km, compared to those unaware of battery aging. Meanwhile, global optimization algorithms like the dynamic programming and Pontryagin's Minimum Principle, as well as a rule-based method, are compared with the predictive controller, in terms of computational expense and accuracy.

A new analysis framework is developed and applied to assess the benefits of building energy efficiency policies and programs. One of the main advantages of the new energy productivity analysis is that it accounts for both economic and energy performances of energy efficiency actions using only one metric. Specifically, the approach applies the concept of energy productivity to the building sector and accounts for both value added and energy savings of energy efficiency measures. Moreover, the proposed analysis accounts for all quantifiable benefits of energy efficiency programs including economic, environmental, and social. In this paper, the general guidelines for the energy productivity analysis are first described. Then, the analysis is applied to evaluate energy efficiency renewable energy programs for both existing and new buildings in the Gulf Cooperation Council (GCC) countries. The analysis results indicate that retrofitting the existing building stock can provide significant benefits and can improve the energy productivity of the building sector in all GCC countries and free up large energy volumes and investment potentials to the development of other economic sectors. In particular, the analysis indicates that reduction in energy consumption, peak demand, and carbon emissions due to deep retrofit programs for the existing building stock can double the energy productivity of the GCC region.

At present, the energy structure of China is shifting towards cleaner and lower amounts of carbon fuel, driven by environmental needs and technological advances. Nuclear energy, which is one of the major low-carbon resources, plays a key role in China's clean energy development. To formulate appropriate energy policies, it is necessary to conduct reliable forecasts. This paper discusses the nuclear energy consumption of China by means of a novel fractional grey model FAGMO(1,1,k). The fractional accumulated generating matrix is introduced to analyse the fractional grey model properties. Thereafter, the modelling procedures of the FAGMO(1,1,k) are presented in detail, along with the transforms of its optimal parameters. A stochastic testing scheme is provided to validate the accuracy and properties of the optimal parameters of the FAGMO(1,1,k). Finally, this model is used to forecast China's nuclear energy consumption and the results demonstrate that the FAGMO(1,1,k) model provides accurate prediction, outperforming other grey models.

The industry is a crucial actor towards the energy transition with the possibility to adopt new energy strategies including a prosumer model. However, industries are struggling to adopt smart energy approaches, and initiatives supporting them should be improved. To enhance industrial participation in energy transition, it is required to assess the optimal energy infrastructure considering its economic advantages and associated risks. Up to date, the literature dealing with energy sizing optimisation does not consider the time evolution of parameters or the uncertainty linked to the energy framework. The objective of this paper is to fill this literature gap by proposing a novel complete methodology to optimise the design and operation of the energy infrastructure for its lifetime while assessing its uncertainty and risk through an uncertainty analysis, as well as to identify the inputs causing it by a two-stage sensitivity analysis. This framework is applied to a case study based on a real industrial manufacturing SME. The results indicate that the proposed methodology produces robust results in front of the present uncertainties, being energy price the one that causes most of it and thus the one more attention should be paid to when evaluating energy investment decisions. Objectius de Desenvolupament Sostenible::9 - Indústria, Innovació i Infraestructura Objectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant

Nowadays, new developments of future aircraft focus on the electric and electric – hybrid aircraft, while features, especially the specific energy and thermal instability of the available accumulator technology cause serious problems. This paper aims to define, discuss, and evaluate how the radically new, so-called out-of-the-box solutions may improve energy efficiency, and reduce the energy consumptions of future aircraft and their operations. This study introduces a novel methodology for (i) evaluation of the energy efficiency, (ii) estimation of the possible energy consumption reduction, (iii) estimation of the used energy reductions. The practical part shortly describes the radically new technologies and solutions that can be applied for reducing the energy consumption of (iv) aircraft development, and (v) aircraft operations, including the ground and terminal operations, as well as discuss some results of two radically new concepts, namely maglev assisted take-off and landing and hybrid-electric aircraft with unconventional structural solutions. Among the developed out-of-the-box solutions, the required energy can be reduced significantly by the “undercarriage-less configuration concept”, and “electric UAV supporter concept” up to 21% and 10% respectively. In addition to this, the fuel burn can be reduced by 15–20% by the “Cruiser-Feeder Concept”.

Distributed drive electric vehicles are regarded as a broadly promising transportation tool owing to their convenience and maneuverability. However, reasonable and efficient allocation of torque demand to four wheels is a challenging task. In this paper, a deep reinforcement learning-based torque distribution strategy is proposed to guarantee the active safety and energy conservation. The torque distribution task is explicitly formulated as a Markov decision process, in which the vehicle dynamic characteristics can be approximated. The actor-critic networks are utilized to approximate the action value and policy functions for a better control effect. To guarantee continuous torque output and further stabilize the learning process, a twin delayed deep deterministic policy gradient algorithm is deployed. The motor efficiency is incorporated into the cumulative reward to reduce the energy consumption. The results of double lane change demonstrate that the proposed strategy results in better handling stability performance. In addition, it can improve the vehicle transient response and eliminate the static deviation in the step steering maneuver test. For typical steering maneuvers, the proposed direct torque distribution strategy significantly improves the average motor efficiency and reduces the energy loss by 5.25%–10.51%. Finally, a hardware-in-loop experiment was implemented to validate the real-time executability of the proposed torque distribution strategy. This study provides a foundation for the practical application of intelligent safety control algorithms in future vehicles.

The optimization and monitoring of the energy consumption of machinery lead to a sustainable and efficient industry. For this reason and following a digital twin strategy, an online data-driven energy modeling approach with adaptive capabilities has been proposed and described throughout this paper. This approach is useful in developing robust energy management systems that enhance the energy efficiency of industrial machinery. In this way, the dynamic behavior of their energy consumption is modeled without using phenomenological laws. In contrast, traditional methodologies hardly consider such dynamic behavior or use an exhaustive modeling process. The proposed approach includes an adaptive mechanism to consider the natural degradation of machinery. This mechanism is based on a concept drift detector, which detects when the current consumption of the machine is not correctly represented by the model estimation and adapts the model to account for these new behaviors. The concept drift detector has broad applicability in the face of reducing maintenance costs, measuring the impact and evolution of either abnormal behaviors (e.g., failures) or degradation, and identify which elements change. The proposed methodology has been validated in an industrial testbed. An experiment with three emulated concept drifts was carried out in the testbed. As a result, the proposed adaptive approach obtained more than doubled the fit rate of the energy prediction/estimation compared to the non-adaptive model and successfully detected these changes in energy consumption., This work has been supported by the Doctorats Industrials program from the Catalan Government (2019 DI 4). The authors would like to thank the companies of the Inzu Group, Etxe-tar, and Ikergune, for the support related to high-productivity systems.

The analysis presented in this paper evaluates the impact of both short-term and long-term stay home living on the energy consumption of Saudi residential building stock. The analysis combines monitored data obtained for a sample of housing units as well as the results from a validated Saudi residential building stock model. In particular, a bottom-up building stock modeling analysis approach is used to estimate the energy impact of the 2020 stay home order imposed due to COVID-19 in most of Saudi Arabia's regions between March 15 and June 15, 2020. Moreover, the potential influences of long-term stay home patterns on the cost-benefits of energy retrofit programs are investigated targeting Saudi existing housing stocks. The analysis results indicate that when normalized to weather, the 2020 lockdown has resulted in a 16 % increase in electricity consumption compared to the 2019 level specific to the entire Saudi housing stock. Most of this increase is associated with the higher energy end-use of lighting, appliances, and air conditioning associated with increased occupancy levels during daytime hours. For a transition to long-term stay home living, the results of the analysis show that the energy consumption of the Saudi housing stock is estimated to increase by 13.5 % compared to the current occupancy pattern. Moreover, the analysis indicates that the cost-benefits for energy efficiency retrofits are enhanced with long-term stay home living.

The distribution of energy consumption by sector shows that the consumption rate of the building is very high. On a worldwide scale, the rate varies between 30 and 40%. In Algeria and according to the energy balance published by the Ministry of Energy, the residential household sector represents of about 42% of the total energy consumption. The main objective of this paper is to study the performance of the grid-connected photovoltaic system and the energy balance in the residential building. A case study is applied in the northern region of Algeria. The photovoltaic system supplies the house with electricity during the sunny days, and during the night or the cloudy-days, the house is powered by grid. The calculation of energy performance is based on the optimization, rationalization and saving energy approach. This takes into account the energy profiles of residential homes, real data and meteorological conditions. The results show clearly that the use of saving energy and grid-connected photovoltaic system allows a positive annual electricity balance of the studied residential house. The PV system generated 67.6% of the overall energy used in the house. Only, 33.4% are purchased from the grid. An energy positive balance of 2 kWh/day is observed.

Energy planning in many developing Asian countries has neglected the structure of energy demand and how it is likely to evolve as development takes its path. The limited availability of energy consumption data makes it very difficult to assess the energy savings potential at end-use level. Energy planning requires the formulation of a proper modelling framework that supports the definition of energy policies. From the different approaches available, Input - Output (I-O) models are particularly useful, since they allow considering different impacts that can be consistent with different energy policy options. This paper proposes a novel I-O modelling framework by introducing a bottom-up approach into an I-O model which is combined with technical data for the holistic assessment of energy efficient technologies in the residential sector, which can assist energy decision-makers of India on the appraisal of the future impacts of the current national energy saving targets. A large size platform of real data has also been gathered considering different data sources, namely the household building stock characterization, the number of operating days according to the climatic regions of India, the lifetime and the investment cost of equipment. Finally, the main results are discussed and future research opportunities are identified.

This paper examines the relationship between energy consumption and economic growth for the United States both at a country and at a sectoral level (Industry, Residential, Electric Power and Transportation) using an asymmetric threshold cointegration approach and monthly data from January 1991 to May 2016. Granger causality tests support a neutrality hypothesis for all sectors, except for the case of total consumption at the country level where a unidirectional causality is running from energy consumption to economic growth.

© 2018 Elsevier Ltd A considerable amount of energy is consumed by machine tools during the run-time operations such as tool change and tool path. The value of this part of energy is affected by the processing sequence of features of a part (PSFP) because the tool path and tool change plan will vary based on the different PSFP. This paper firstly aims to understand the relationship between the PSFP and the energy consumption of tool change and tool path during the feature transitions. Then, a model is introduced for the single objective optimisation problem that minimises the energy consumption of machine tools during the feature transitions which include all the tool path and tool change operations. Finally, optimisation approaches including depth-first search and genetic algorithm are modified and applied to find the optimal PSFP which results in the minimisation of the energy consumption of feature transitions (EFT). In the case study, the optimal and near-optimal sequences of features, in terms of the minimum EFT, of a part which has 15 actual features and is processed by a machining centre have been found. The optimal PSFP achieves a 28.60% EFT reduction, which validates the effectiveness of the developed model and optimisation approaches. Besides, a 27.95% time reduction of feature transitions benefits from the EFT minimisation.

Energy consumption for hot water production is a major draw in high efficiency buildings. Optimizing this has typically been approached from a thermodynamics perspective, decoupled from occupant influence. Furthermore, optimization usually presupposes existence of a detailed dynamics model for the hot water system. These assumptions lead to suboptimal energy efficiency in the real world. In this paper, we present a novel reinforcement learning based methodology which optimizes hot water production. The proposed methodology is completely generalizable, and does not require an offline step or human domain knowledge to build a model for the hot water vessel or the heating element. Occupant preferences too are learnt on the fly. The proposed system is applied to a set of 32 houses in the Netherlands where it reduces energy consumption for hot water production by roughly 20% with no loss of occupant comfort. Extrapolating, this translates to absolute savings of roughly 200 kWh for a single household on an annual basis. This performance can be replicated to any domestic hot water system and optimization objective, given that the fairly minimal requirements on sensor data are met. With millions of hot water systems operational worldwide, the proposed framework has the potential to reduce energy consumption in existing and new systems on a multi Gigawatt-hour scale in the years to come. ispartof: Energy vol:144 pages:159-168 status: published

Weather can have a profound effect on energy consumption particularly with respect to hot and cold temperatures, driving residential and commercial energy demand. The cooling and heating degree day methodology has been often been regarded as a reliable means to account for this effect for purposes of normalization and econometric analysis. However, when applied within the context of international cross-country comparison, this methodology suffers from two majors limitations: the lack of an appropriate international database that encompasses degree days at a functional spatial and temporal aggregation using various reference temperatures, and second, the existing methodologies only account for the effect of temperature and ignore the potential effect of other climatic factors such as humidity and solar radiation. This paper addresses these issues by presenting a new database of population-weighted degree days for 147 countries for 1948–2013 at various reference temperatures based on multiple thermal comfort indices. The database was mainly developed to compare the influence of weather on energy use across countries. This is important for policy because it defines the playing field for potential intervention to improve energy efficiency and productivity. It also puts countries on equal footing when it comes to participating in international energy and environmental negotiations.

The Portuguese ceramic industry is characterized by SME industries with high thermal energy consumption. Thus, the implementation of energy efficiency measures should include no-cost solutions, which are generally not explored in literature. This paper analyses the energy profile of different ceramic sub-sectors to identify potential improvements to meet low-cost competition in the market. Preliminary results of the statistical analysis suggest inefficient energy management, with a particular focus on natural gas consumption. The studied factories could easily improve profits and reduce energy consumption through good kiln operating practices. Optimization of kiln ceramic load (2–18 %) and efficient combustion in the gas burners (1–11 %) are examples of no-cost strategies that promote significant energy savings and carbon emissions reduction.

With rapid urbanization and economic development, the world has been experiencing an unprecedented increase in energy consumption and greenhouse gas (GHG) emissions. While reducing energy consumption and GHG emissions is a common interest shared by major developed and developing countries, actions to enable these global reductions are generally implemented at the city scale. This is because baseline information from individual cities plays an important role in identifying economical options for improving building energy efficiency and reducing GHG emissions. Numerous approaches have been proposed for modeling urban building energy use in the past decades. This paper aims to provide an up-to-date review of the broad categories of energy models for urban buildings and describes the basic workflow of physics-based, bottom-up models and their applications in simulating urban-scale building energy use. Because there are significant differences across models with varied potential for application, strengths and weaknesses of the reviewed models are also presented. This is followed by a discussion of challenging issues associated with model preparation and calibration.

The paper presents the advantages of a new boiler solution for the supercritical power plant with CO2 capture. The MILD oxyfuel combustion (MOFC) combines the advantages of MILD (moderate and intensive low-oxygen dilution) combustion and oxyfuel combustion for the purpose of an effective CO2 capture from fossil fuel based power generation. MOFC application could increase the efficiency of the boiler, increase the purity of the CO2 in flue gases and reduce energy consumption for the recirculation of CO2. It affects the overall net energy efficiency penalty associated with the CO2 capture in comparison to the oxyfuel combustion technology. Thermodynamic analysis of an integrated MOFC power plant with CO2 capture are presented. The data concerning the new design of the boiler are obtained from CFD modelling. Two case studies are performed, and in each of them three configurations of supercritical power plant are modelled. First two are the reference power plants, including the conventional power plant without CO2 capture and oxyfuel combustion power plant with CO2 capture. The third case is the MOFC boiler application within the same power plant. The thermodynamic parameters are compared, and detailed study of energy efficiency penalty is presented. Based on the presented results it can be noticed that the application of the MOFC technology allows to increase the overall net energy efficiency by about 2% points. Additionally the usefulness of the proposed system approach (based on input-output analysis) for the energy analysis of complex energy systems have been proven.

Advanced control strategies can enable energy flexibility in buildings by enhancing on-site renewable energy exploitation and storage operation, significantly reducing both energy costs and emissions. However, when the energy management is faced shifting from a single building to a cluster of buildings, uncoordinated strategies may have negative effects on the grid reliability, causing undesirable new peaks. To overcome these limitations, the paper explores the opportunity to enhance energy flexibility of a cluster of buildings, taking advantage from the mutual collaboration between single buildings by pursuing a coordinated approach in energy management. This is achieved using Deep Reinforcement Learning (DRL), an adaptive model-free control algorithm, employed to manage the thermal storages of a cluster of four buildings equipped with different energy systems. The controller was designed to flatten the cluster load profile while optimizing energy consumption of each building. The coordinated energy management controller is tested and compared against a manually optimised rule-based one. Results shows a reduction of operational costs of about 4%, together with a decrease of peak demand up to 12%. Furthermore, the control strategy allows to reduce the average daily peak and average peak-to-average ratio by 10 and 6% respectively, highlighting the benefits of a coordinated approach.

Energy demand-side management, especially empowered by the fine-grained smart meter data, plays a significant role in the rational allocation of energy, monitoring and supervision of energy consumption behaviors. Through the in-depth demand analysis including quantification of energy consumption dynamics and consumer preferences, energy decision-makers can develop reasonable and forethoughtful energy efficiency plans and demand-response programs. Previous work in energy-demand behavioral research relied primarily on ideal socio-economic models or data-driven approaches, both of which lack flexibility, intuition and interpretability. This paper proposes a novel spatio-temporal visual analysis approach for urban energy consumption pattern discovery in order to identify energy-saving potentials, plan energy supply and improve energy efficiency. In this approach, energy consumption time series are embeded into a two-dimensional scatterplot for coordinated visual exploration. Users can interactively explore and discover different patterns for decision-making purposes. In addition, we propose the method for modeling energy demand shift patterns based on a potential flow method and integrate it into a pattern exploration tool. The proposed approach is comprehensively evaluated through empirical studies using the real-world electricity consumption data from Pudong district, Shanghai. We identify five typical energy consumption patterns and demand shift patterns across different geographical locations, which can be well interpreted by the knowledge of energy consumption in the area of interest. The results demonstrate the effectiveness of the proposed approach and the tool. This tool can be integrated into smart energy systems for a better understanding of user energy consumption behaviors and preferences.

A district cooling system (DCS) is superior to conventional air conditioning as it helps to reduce energy consumption and protect the environment by reducing carbon dioxide emissions. The main disadvantages of a DCS are the high initial investment costs and the long payback period. The distribution network (DN) represents a large share of initial investment costs; therefore, it has a great impact on the decision to construct a DCS. In order to ensure the competitiveness of DCS, the DN has to be optimized. In this paper the exergoeconomic concept is applied to evaluate a DN in a DCS. The objective function in the analysis is defined as the exergy based cost of the final product-cold. The exergy-based cost of cold depends on the total annual cost of a DN, the input exergy to the DN, the exergy losses and the exergy destruction. The aim of this study is to find the exergetic optimal pipe diameter and the insulation thickness, as well as the exergoeconomic optimal pipe diameter and the insulation thickness. The analysis was made for different cooling capacities and for two types of pipes: pre-insulated steel pipes, where the insulation material is polyurethane, and polyethylene pipes, without any insulation.

This paper evaluates the energy consumption and battery performance of city transit electric buses operating on real day-to-day routes and standardized bus drive cycles, based on a developed framework tool that links bus electrification feasibility with real-world vehicle performance, city transit bus service reliability, battery sizing and charging infrastructure. The impacts of battery capacity combined with regular and ultrafast charging over different routes have been analyzed in terms of the ability to maintain city transit bus service reliability like conventional buses. The results show that ultrafast charging via frequent short-time boost charging events, for example at a designated bus stop after completing each circuit of an assigned route, can play a significant role in reducing the battery size and can eliminate the need for longer duration charging events that would cause schedule delays. The analysis presented shows that significant benefits can be realized by employing multiple battery configurations and flexible battery swapping practices in electric buses. These flexible design and use options will allow electric buses to service routes of varying city driving patterns and can therefore enable meaningful reductions to the cost of the vehicle and battery while ensuring service that is as reliable as conventional buses.

This paper investigates the impact of data errors on home energy management systems (HEMSs) that reduce energy cost and maintain comfort for residential consumers. In particular, we conduct a sensitivity analysis of HEMS subject to various types of input data such as the predicted energy consumption, the forecasted outdoor temperature, the consumers' comfort settings, static and dynamic operation constraints for home appliances, and the demand response (DR) signal. Using the perturbed Karush-Kuhn-Tucker (KKT) condition equations from the HEMS optimization formulation, we develop a linear sensitivity matrix to assess the impact of data on optimal solutions for: (1) electricity cost; (2) consumer's dissatisfaction cost; (3) the energy consumption for home appliances; and (4) the indoor temperature. The results of a simulation study using the developed sensitivity matrix provide HEMS operators with unique insight into factors that account for the relationships of HEMS operations to the change in the various data. Furthermore, these results can be used to provide insights for residential consumers and to evaluate the security risks of HEMS to cyber attacks through data manipulation.

Nowadays, energy saving as well as energy-conscious design and refurbishments of buildings became the most important actions to be achieved worldwide. The energy performance of a building can be considerably affected by climate. The significance of a ‘design with climate’ approach is highlighted in this paper. The article investigates the impact of climate conditions (focusing on humidity and precipitation) on design decisions. The overall energy performance of the building is achieved by the adopted architectural and technical solutions. In this study the thermal performance of the envelope of nearly zero energy buildings, built from different materials with different moisture load is tested and demonstrated. The change of the specific heat loss coefficient of buildings is presented in function of the building structure (wall and insulation), design (envelope surface to heated volume ratio) and moisture content of materials. In the conclusion the article attempts to give suggestions to stakeholders, decision makers and planners to choose the appropriate envelope structure from moisture resistant, geometry and cost-optimum points of view. Since - the building enclosure is the interface between the interior of the building and the outdoor environment - a building's energy consumption depends on certain envelope design elements to a large extent.

This paper develops a systems-based integrated model for investigating the impacts of various energy technologies as applied to meet specific energy needs in a rural developing village. The model enables the designer to examine of a variety of energy technology components subject to local and global constraints and reports the outcomes in terms of multiple objectives including energy consumption, climate effects, health impacts, cost analyses, and social considerations. It enables accounting for important application factors such as usability, multi-functionality, stacking and incomplete displacement of traditional methods, opportunity costs, effective discount rates, and impact to quality of life. Use of the model to analyze the baseline case of a well-characterized village in Mali revealed the conflicts between social, economic, and environmental objectives that often exist between stakeholders, highlighting the importance of attention to consumer preference. Analysis based on disaggregated energy needs illustrated that often the relative impacts between energy strategies are not immediately evident, suggesting that holistic systems-level analyses are critical before selecting a specific strategy to supply improved energy services to households in a community.

The European Union has set specific targets forreducing energy consumption, shifting the main focus ofEuropean energy policytowards a firm commitment to enhancing energy efficiency. In light of the limited progress reported in Greece's energy efficiency action plan, the objective of this paper is to helpredesignthe near-term national policy framework. In this respect, a risk-orientedoptimisationmodel is developed to support the optimal budgetallocation toenergy efficiency measures, with a view to achieving the 2020 national targets. The transdisciplinary methodological framework initially builds on the Energy Efficiency Action Plans for Greece and involves stakeholder engagement,multiple-criteriadecision making and portfolio analysis. The evaluation of thepolicy instrumentsis based on two criteria, namely energy savings and implementation risk, under a set of technical and financial constraints. Finally, the robustness of both theoptimal policyportfolios and the individual policies composing these portfolios is assessed. The results indicate that, given the financial and technical constraints, the 2020 target cannot be achieved. By stochastically analysing the impact of external uncertainty, three policies emerge as the most robust, while portfolios achieving savings of more than 1000 kTOE until 2020 are found highly vulnerable to underlying uncertainties.

A common problem with Chinese district heating systems is that they supply more heat than the actual heat demand. The reason for this excess heat supply is the general failure to use control devices to adjust the indoor temperature and flow in the building heating systems in accordance with the actual heat demand. This results in 15-30% of the total supplied heat being lost. This paper proposes an integrated approach that aims to reduce the excess heat loss by introducing pre-set thermostatic radiator valves combined with automatic balancing valves. Those devices establish hydraulic balance, and stabilize indoor temperatures. The feasibility and the energy consumption reduction of this approach were verified by means of simulation and a field test. By moving the system from centrally planned heat delivery to demand-driven heat delivery, excess heat loss can be significantly reduced. Results show that once the hydraulic balance is achieved and indoor temperatures are controlled with this integrated approach, 17% heat savings and 42.8% pump electricity savings can be achieved. The energy savings will also have a positive environmental effect with seasonal reductions of 11 kg CO2, 0.1 kg SO2, and 0.03 kg NOx per heating square meter for a typical case in Harbin.

A dynamic simulation model for the micro-cogeneration unit based on ICE (internal combustion engine) is built as a new component on TRNSYS. This model considers dynamic processes including start-up, cool-down, and overheat protection controls. Through an experiment study, the steady and dynamic performances of the unit are presented and analyzed for calibrating the parameters of the dynamic model. Then the model is validated by the experiment data during steady and dynamic processes. The improvement of prediction accuracy of the model by considering dynamic processes is analyzed. The validation results show that the dynamic model can well predict the characteristics of the unit during start-up, cool-down, and overheat protection control processes. The cumulative prediction errors of the dynamic model decrease with the increase of the start–stop interval. By considering the dynamic processes, 5.18% and 3.2% of Qth and ηtotal cumulative prediction errors are decreased respectively when the start–stop interval is 0.5 h. The primary energy saving ratio and CO2 emission reduction ratio increases with the increase of the start–stop interval. The start–stop interval for the unit in this paper should be longer than 0.5 h, or the energy consumption of micro-cogeneration unit would be larger than that of conventional system.

Freeways have become the main subject of energy conservation reviews in China because of their large energy consumption. Decomposition analysis has been widely applied in energy consumption studies. However, most studies have only analyzed the driving forces of energy consumption on the national level, and seldom at smaller levels, e.g. for departments like toll stations and maintenance centers. Based on the characteristics of the freeway operation period, this paper serves as a preliminary attempt at applying the logarithm mean Divisia index method I on the department level to analyze the energy consumption of freeways during the operation period. To elucidate the evolution of energy consumption during the operational period, the logarithm mean Divisia index analysis was performed to disentangle the energy consumption based on a real case in Guangdong from 2005 to 2013. The analyses establish that the traffic volume influences energy consumption. The results show that some departments can influence the total energy consumption. For example, the tunnels and toll stations are key factors that influence the total energy consumption of the whole road. The decomposition in the departments revealed that energy efficiency improvements caused by the use of alternate materials and energy-saving technologies mainly contributed to energy conservation.

Biofuel is considered a promising alternative to traditional liquid transportation fuels. The large-scale substitution of biofuel can greatly enhance global energy security and mitigate greenhouse gas emissions. One major concern of the broad adoption of biofuel is the intensive energy consumption in biofuel manufacturing. This paper focuses on the energy efficiency improvement of biofuel feedstock preprocessing, a major process of cellulosic biofuel manufacturing. An improved scheme of the feedstock preprocessing considering work-in-process particle separation is introduced to reduce energy waste and improve energy efficiency. A scheduling model based on the improved scheme is also developed to identify an optimal production schedule that can minimize the energy consumption of the feedstock preprocessing under production target constraint. A numerical case study is used to illustrate the effectiveness of the proposed method. The research outcome is expected to improve the energy efficiency and enhance the environmental sustainability of biomass feedstock preprocessing.

Many advances have been made in building technology to help save energy, but influencing the behavior of the occupants is still necessary to achieve low-energy use targets. One of the most practical ways to influence and change occupant behaviors is through incentives. Developing incentives for energy-saving and quantifying the impact of occupant behaviors are both active areas of research. In this paper, we propose a data analytics framework for detecting changes in occupant behaviors, which will help build an analytics feedback loop from behavior impact to incentive design. The framework has two major parts. The first forecasts energy consumption for each occupant, while the second determines a probability distribution for changes in energy consumption. The parts are interchangeable with other existing machine learning and statistical methods. A specific instantiation of the framework, using kernel ridge-regression for forecasting and k-means to find an empirical behavior distribution, is described in detail. An HVAC use-case with 5 different incentivized behaviors is used as an example to show that the framework can detect behavior changes induced by incentives. Furthermore, we show that some simpler behavior-change detection methods do not work, further justifying the use of advanced analytics.