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According to the latest energy efficiency European directive (EED 2023/1791/EU), the expected energy renovation rate of at least 3% of the buildings' floor area each year towards nearly zero-energy buildings (nZEBs) is extended to include public buildings not only of the central government (as per the first EED 2012/27/EU) but also of regional and local authorities. This poses a great challenge, especially for Municipalities that often manage large building stocks with high energy demands. In response to this challenge, this paper presents the application of the so-called "typology approach" for conducting public buildings' energy renovation plans at the local level. A computational survey is initially introduced to decide the optimal set of building-stock clustering criteria among all possible combinations, involving the minimization of the RMSE index regarding the primary energy consumption of each building. For a representative building from each identified typology, the key performance indicators (KPIs) are computed for alternative energy-upgrading scenarios. Exploiting the IMPULSE Interreg-MED project tools, the KPIs from each representative building are at first extrapolated to all buildings of the examined stock and, finally, a gradual energy renovation plan is automatically produced based on user-defined decision parameters including the required annual renovation rate. The methodology is applied for the case of the Municipality of Hersonissos in Greece. For the specific 44-buildings' stock it was found that the optimal clustering set included four criteria, building use, construction year, heating, and a cooling system, leading to 15 building typologies. Finally, assuming a 7% renovation rate per year, a 12-year gradual renovation (nZEB transformation) plan is obtained foreseeing an 85% CO2 emissions' reduction. [ABSTRACT FROM AUTHOR]

To achieve climate and energy goals in the building sector, the current pace of renovating existing structures must double, overcoming prevailing barriers. Key Performance Indicators play a pivotal role in science-based decision making, emphasizing both direct and indirect benefits of building retrofits. The authors aim to contribute to proper metric identification for multi-perspective building performance assessment and formulate a methodology supporting energy planning decisions. They introduce the Global Cost per Emission Savings (GCES), an aggregated indicator encompassing both public (CO2 emissions) and private (global cost) perspectives of diverse retrofit technologies for building HVAC systems. Applied to the Italian residential building stock via the Reference Building approach, the methodology is tested using condensing gas boilers, biomass boilers, and electric heat pumps, revealing diverse environmental and economic performances. Addressing the establishment of effective decision-support tools for policymakers, the paper explores the potential impact of various policies on the favorability of technologies. Different policy scenarios are delineated to analyze how distinct approaches may influence the attractiveness of technologies. Notably, in the baseline scenario, biomass boilers hold an advantage over heat pumps according to the GCES index. However, scenarios involving technology-specific incentives or a greenhouse gases emission tax failed to alter the technological ranking, leaving heat pumps financially uncompetitive. In contrast, the TXPM scenario positions heat pumps as the most financially appealing option, penalizing biomass boilers for high particulate matter emissions. [ABSTRACT FROM AUTHOR]

Building energy planning is a challenging task in the current mounting climate change scenario because the sector accounts for a reasonable percentage of global end-use energy consumption, with a one-fifth share of global carbon emissions. Energy planners rely on physical model-based prediction tools to conserve energy and make decisions towards decreasing energy consumption. For precise forecasting, such a model requires the collection of an enormous number of input variables, which is time-consuming because not all the parameters are easily available. Utilities are reluctant to share retrievable consumer information because of growing concerns regarding data leakage and competitive energy markets. Federated learning (FL) provides an effective solution by providing privacy preserving distributed training to relieve the computational burden and security concerns associated with centralized vanilla learning. Therefore, we aimed to comparatively analyze the effectiveness of several data-driven prediction algorithms for learning patterns from data-efficient buildings to predict the hourly consumption of the building sector in centralized and FL setups. The results provided comparable insights for predicting building energy consumption in a distributed setup and for generalizing to diverse clients. Moreover, such research can benefit energy designers by allowing them to use appropriate algorithms via transfer learning on data of similar features and to learn personalized models in meta-learning approaches. [ABSTRACT FROM AUTHOR]

This study conducted an in-depth analysis of the performance of the largest Grid-Connected Solar Photovoltaic System in Burkina Faso from 2019 to 2021. The research utilized measured data and simulated the plant's performance using the PVGIS database. The results revealed that the months with high solar radiation were the most energy-productive, indicating a direct correlation between solar irradiance and energy generation. During the rainy season (July and August), the PV plant exhibited the highest conversion efficiency. Conversely, the hot season (March and April) was associated with the lowest conversion efficiencies, with module temperatures reaching approximately 47 °C. Efficiency decreased from 12.29% in 2019 to 12.10% in 2021. The system's performance ratio ranged from 80.73% in 2019 to 79.36% in 2021, while the capacity factor varied from 19.89% in 2019 to 19.33% in 2021. The final yield, measured in hours per day, was 4.89 h/d in 2019, 4.61 h/d in 2020, and 4.92 h/d in 2021. These findings highlight the deterioration in the performance of the Zagtouli PV plant over time. The study emphasizes the utility of using PVGIS-SARAH2 to forecast solar radiation and estimate energy output in PV systems. A semi-automatic cleaning system is used to clean the modules. This cleaning mechanism is inefficient because it is inconsistent. To increase the PV plant's effectiveness, improved cleaning systems with more advanced mechanisms are required. This research, the first of its kind on the largest PV power plant connected to Burkina Faso's national grid, serves as a valuable model for other power plants currently under construction or in the planning stages. [ABSTRACT FROM AUTHOR]

In accordance with South Korea's recent 2030 Carbon Neutral Plan, an 8GW offshore wind farm is planned for construction in the South-west Sea. Therefore, it is expected that large-scale wind turbines will be installed, and these turbines must operate stably, even when there are instantaneous voltage fluctuations in the power system. The grid code is described for the low-voltage-ride-through (LVRT) and high-voltage-ride-through (HVRT) functions, and a test facility that can perform both LVRT and HVRT tests is essential. In the case of LVRT/HVRT test facilities developed by the existing RLC (impedance component) method, it may be difficult to test large-scale wind turbines due to problems such as power quality, frequent failures and narrow short-circuit capacity ranges. Therefore, to solve such problems, this paper proposes an LVRT/HVRT test facility of the autotransformer type, which is capable of outputting the desired voltage range by changing the wiring method and tap position. Specifically, in order to implement the test facility of the autotransformer type, which is able to output the desired voltage range by changing the wiring method and tap according to the LVRT/HVRT test status, this paper presents an impedance determination algorithm (two-step layer impedance determination algorithm) of auto-transformer based on the fault-current analysis and operation strategy at a real LVRT/HVRT testing evaluation facility. [ABSTRACT FROM AUTHOR]

With the enhancement of people's awareness of heritage protection, research communities focusing on the natural ventilation of the layouts of ancient buildings have paid more attention to the planning and protection of these buildings. Based on the relationship between the natural ventilation environment and the layout of the building, we can reduce the adverse effects of energy consumption and outdoor wind, improve the environment and quality around the building, and achieve harmony between humans and nature. In this study, Fluent software was used to simulate the wind environment of Xingguo Temple. The advantages of combining computer simulation software with ancient building protection planning are illustrated by comparing the wind environment before and after the temple reconstruction with Fluent software. Through the simulation of the building's wind environment, some suggestions are put forward for the early layout of the outdoor environment in the ancient building reconstruction planning area. [ABSTRACT FROM AUTHOR]

Well construction operations require continuous complex decision-making and multi-step action planning. Action selection at every step demands a careful evaluation of the vast action space, while guided by long-term objectives and desired outcomes. Current human-centric decision-making introduces a degree of bias, which can result in reactive rather than proactive decisions. This can lead from minor operational inefficiencies all the way to catastrophic health and safety issues. This paper details the steps in structuring unbiased purpose-built sequential decision-making systems. Setting up such systems entails representing the operation as a Markov decision process (MDP). This requires explicitly defining states and action values, defining goal states, building a digital twin to model the process, and appropriately shaping reward functions to measure feedback. The digital twin, in conjunction with the reward function, is utilized for simulating and quantifying the different action sequences. A finite-horizon sequential decision-making system, with discrete state and action space, was set up to advise on hole cleaning during well construction. The state was quantified by the cuttings bed height and the equivalent circulation density values, and the action set was defined using a combination of controllable drilling parameters (including mud density and rheology, drillstring rotation speed, etc.). A non-sparse normalized reward structure was formulated as a function of the state and action values. Hydraulics, cuttings transport, and rig state detection models were integrated to build the hole cleaning digital twin. This system was then used for performance tracking and scenario simulations (with each scenario defined as a finite-horizon action sequence) on real-world oil wells. The different scenarios were compared by monitoring state–action transitions and the evolution of the reward with actions. This paper presents a novel method for setting up well construction operations as long-term finite-horizon sequential decision-making systems, and defines a way to quantify and compare different scenarios. The proper construction of such systems is a crucial step towards automating intelligent decision-making. [ABSTRACT FROM AUTHOR]

Reactive and biased human decision-making during well construction operations can result in problems ranging from minor inefficiencies to events that can have far-reaching negative consequences for safety, environmental compliance and cost. A system that can automatically generate an optimal action sequence from any given state to meet an operation's objectives is therefore highly desirable. Moreover, an intelligent agent capable of self-learning can offset the computation and memory costs associated with evaluating the action space, which is often vast. This paper details the development of such action planning systems by utilizing reinforcement learning techniques. The concept of self-play used by game AI engines (such as AlphaGo and AlphaZero in Google's DeepMind group) is adapted here for well construction tasks, wherein a drilling agent learns and improves from scenario simulations performed using digital twins. The first step in building such a system necessitates formulating the given well construction task as a Markov Decision Process (MDP). Planning is then accomplished using Monte Carlo tree search (MCTS), a simulation-based search technique. Simulations, based on the MCTS's tree and rollout policies, are performed in an episodic manner using a digital twin of the underlying task(s). The results of these episodic simulations are then used for policy improvement. Domain-specific heuristics are included for further policy enhancement, considered factors such as trade-offs between safety and performance, the distance to the goal state, and the feasibility of taking specific actions from specific states. We demonstrate our proposed action planning system for hole cleaning, a task which to date has proven difficult to optimize and automate. Comparing the action sequences generated by our system to real field data, it is shown that it would have resulted in significantly improved hole cleaning performance compared to the action taken in the field, as quantified by the final state reached and the long-term reward. Such intelligent sequential decision-making systems, which use heuristics and exploration–exploitation trade-offs for optimum results, are novel applications in well construction and may pave the way for the automation of tasks that until now have been exclusively controlled by humans. [ABSTRACT FROM AUTHOR]

The energy industry has been trying to reduce the use of fossil fuels that emit carbon and to proliferate renewable energy as a way to respond to climate change. The attempts to reduce carbon emissions resulting from the process of generating the electric and thermal energy needed by a building were bolstered with the introduction of the concept of nZEB (nearly zero-energy building). In line with such initiatives, the South Korean government made it mandatory for new buildings to have an nZEB certificate as a way to promote the supply of renewable energy. The criteria for Energy Independence Rate, which is one of the nZEB certification criteria in South Korea, is to maintain the share of renewable energy as at least 20% of the primary energy sources for the building. For a new building in South Korea to have an nZEB certificate, it is required to establish an energy plan that would allow the building to meet the Energy Independence requirement. This optimally reflects the cost of installation for renewable energy facilities and the cost of purchasing energy from external sources, such as the national grid or district heating companies. In South Korea, the base retail rate of energy is calculated based on the peak demand per hour over the year, rather than the contracted energy. This has produced difficulties in standardizing the process with a mathematical model; in addition, there have not been many preceding studies that could be used as a reference. In this regard, this paper analyzed a modeling strategy for developing a realistic yet optimized energy plan in consideration of the unique conditions of the retail energy rates of South Korea, and analyzed the impact of the rates based on peak demands upon the total energy plan. In this study, our research team analyzed the electric billing system, conducted a case study, and analyzed the impact of the billing system that is based on the peak demand upon the optimal cost. By utilizing the restrictions for reaching the 20% Energy Independence goal, this paper calculated the proper energy supply facility capacity for renewable energy. Then, the cases in which the maximum demand modeling was used and the cases without one were compared to confirm the cost benefits observable when the suggested model is added or implemented. [ABSTRACT FROM AUTHOR]

In order to solve the vulnerability problem of the integrated energy system in the transportation industry, a vulnerability cascade propagation model based on the Petri net is proposed. The article comprehensively considers the configuration of the energy system, constructs the cascade propagation-based function Petri net model using a hierarchical modelling approach, and performs vulnerability cascade propagation analysis using Matlab on this basis. However, the integrated energy system of the transportation industry is complex and extensive, and it is not easy to model the Petri network of the whole transportation industry, which will be continued in-depth in the subsequent research. The study results show that the energy system's vulnerability keeps changing with the growth of time, and the factors in maintaining the equilibrium vary from one subsystem to another. In addition, the ringed structure is more vulnerable compared to the acyclic structure, and the vulnerability cascade propagates faster for the ringed structure than the acyclic structure. The results of the study contribute to the scientific development of integrated energy system planning and construction for the transportation industry and provide a reference for the rehabilitation and construction of energy systems. [ABSTRACT FROM AUTHOR]

The support offered to local authorities in this work consisted of technical and economic training on the integration of energy efficiency measures as well as the development of tools (guidelines, a decision support tool, databases and a visualization platform) that allow local authorities to prepare their building renovation plans. These tools are found very useful for local authorities, particularly in the case of Teruel province, characterised by many small municipalities whose local governments do not have the technical staff to undertake this type of planning. As a result of this work, an energy action plan was elaborated for the implementation of 96 energy efficiency measures in public buildings of Teruel province. The execution of this plan would allow energy savings of 1.3 MWh/year and 245 tonsCO2e/year of CO2 emission reduction, involving an investment of EUR 1.2 M. The close collaboration with public authorities made it possible to assess the strengths and weaknesses of using the developed tools. One of the barriers found is the availability of accurate building data (e.g., regarding envelope features) necessary for the decision support tool. [ABSTRACT FROM AUTHOR]

The purpose of the article is to define the risk factors in cogeneration projects and to demonstrate that a lack of sufficient identification of risks in different phases affects project implementation. A theoretical study is conducted, which aims to identify risk factors in cogeneration projects, based on case studies of such projects in Poland. The study offers a view at CHP (combined heat and power) projects as extremely dependent on the external environment of the organisation. These projects are subject to many external regulations due to their environmental impact and dynamically changing technical aspects. The biggest technical errors occur at the planning and construction stages. The biggest economic and financial risks occur at the execution stage after 2% and 3% of additional design costs occur, respectively. The authors estimated the risks at different stages of the project and concluded that the total cost of failure in correct identification of the risks at the planning stage exceeded PLN 1.5 billion, which amounted to almost 60% of the total additional costs of materialised project risk. Consequently, the biggest challenges in the area of CHP project management at the planning stage are a thorough identification of risks, and the pricing and planning reactions to risk. [ABSTRACT FROM AUTHOR]

The popularity of electric vehicles has been limited by factors such as range, long charging times and fast power failure in winter. In order to overcome these challenges, battery swapping stations (BSS) have been constructed and greatly promoted in recent years. In this paper, the related literature on electric vehicle service is reviewed and the co-occurrence of keywords is analyzed using CiteSpace. The literature is classified according to clustering results and recurring themes, such as the location of BSS, inventory decisions, charging strategies and BSS assignment. In each topic, typical optimization models and algorithms proposed in previous studies are summarized. Then, this paper gives a case about the business model and revenue capacity calculation of BSS. Finally, it points out the future research direction of battery swapping stations for electric vehicles. [ABSTRACT FROM AUTHOR]

Buildings and the mobility sectors are the two sectors that currently utilize large amount of fossil-based energy. The aim of the paper is to, critically analyse the integration of electric vehicles (EV) energy load with the building's energy load. The qualitative and quantitative methods are used to analyse the nearly/net zero energy buildings and the mobility plans of the Europe along with the challenges of the plans. It is proposed to either include or exclude the EV load within the building's energy load and follow the emissions calculation path, rather than energy calculation path for buildings to identify the benefits. Two real case studies in a central European climate are used to analysis the energy performance of the building with and without EV load integration and the emissions produced due to their interaction. It is shown that by replacing fossil-fuel cars with EVs within the building boundary, overall emissions can be reduced by 11–35% depending on the case study. However, the energy demand increased by 27–95% when the EV load was added with the building load. Hence, the goal to reach the nearly/net zero energy building target becomes more challenging. Therefore, the emission path can present the benefits of EV and building load integration. [ABSTRACT FROM AUTHOR]

Construction time and time overruns for infrastructure projects have been frequently studied; however, the construction time of power plants has not been studied. This lack of study is problematic, as more renewable energy power plants, such as wind and solar, are planned for many jurisdictions. Accurately estimating the construction time of a power plant will assist construction planning, budget estimates, and policy development encouraging the use of more renewable sources. The construction times of utility scale power plants in Canada were studied using publicly available data. Multiple linear regression analysis techniques were applied to the data to generate construction time estimation functions for all power plants together, and for individual technologies. The analyses reveal that construction time is sensitive to jurisdiction and the decade of construction, indicating that decisions made by individual Canadian provincial governments at different times had statistically significant impacts on construction time. The analyses also indicated that construction time is a strong function of installed capacity, independent of technology. This finding suggests that large solar or wind energy facilities will encounter longer construction times similar to large hydroelectric facilities. [ABSTRACT FROM AUTHOR]

Buildings account for a significant portion of our overall energy usage and associated greenhouse gas emissions. With the increasing concerns regarding climate change, there are growing needs for energy reduction and increasing our energy efficiency. Forecasting energy use plays a fundamental role in building energy planning, management and optimization. The most common approaches for building energy forecasting include physics and data-driven models. Among the data-driven models, deep learning techniques have begun to emerge in recent years due to their: improved abilities in handling large amounts of data, feature extraction characteristics, and improved abilities in modelling nonlinear phenomena. This paper provides an extensive review of deep learning-based techniques applied to forecasting the energy use in buildings to explore its effectiveness and application potential. First, we present a summary of published literature reviews followed by an overview of deep learning-based definitions and techniques. Next, we present a breakdown of current trends identified in published research along with a discussion of how deep learning-based models have been applied for feature extraction and forecasting. Finally, the review concludes with current challenges faced and some potential future research directions. [ABSTRACT FROM AUTHOR]

The introduction of parametric tools has made a strong shift within a traditional approach to urban planning and building design, including the creation of a design awareness zone where environmental issues are concerned. This approach also uses sufficient data to be used already at the concept stage and provides initial interdisciplinary solutions. Analyses from the very initial stages allow the inclusion of smart energy choices influencing the massing, architectural features, proportions, flexibility of design, and economics. This is only a threshold; there is still a place for further development and more accurate analyses leading to the construction of buildings and urban areas with a stronger input of sustainable solutions, as existing approaches have certain limitations. This path has been followed in several research grants conducted at the Faculty of Architecture Warsaw University of Technology, and later on developed as a co-operation area with various stakeholders. Outside the general state of art, this paper will include two case studies which were provided as a concept design for prospective investors. Both locations are in the Warsaw Downtown Area, and analyses include algorithmic models dealing with the optimisation of the buildings' forms, urban scale sun radiation levels, shadow and wind analyses indicating use of sunlight energy and wind as alternative energy sources. One of the case studies contains Pareto Front including both single- and multi-criteria optimization methods for analysing energy and economic efficiency issues, pointing out the best case solutions. [ABSTRACT FROM AUTHOR]

Combined heat and power (CHP), an efficient heating method with cascades use of energy, accounts for approximately 50% of the heat sources in northern China. Many researchers have made significant efforts to improve its energy efficiency and environmental effects with important achievements. Given that the system produces heat and electricity at the same time, this study focuses on the role of CHP in the holistic urban energy system and points out the mismatch between the demand and supply sides of urban energy systems by using the heat-to-power ratio as a parameter. The calculation method and characteristics of the supply side heat-to-power ratio of eight heating methods and the maximum demand side heat-to-power ratio for 19 cities in northern China are displayed. After the analysis, it is concluded that (1) the maximum demand side heat-to-power ratio in the cities varies from 1.0 to 5.9, which is affected by the location and social, economic, and industrial structures. (2) In most of the cities, with the current energy structure, the demand side heat-to-power ratios are always larger than the supply side heat-to-power ratios. (3) The reduction in heating demand, surplus heat recovery, and the use of a highly efficient electric heating method, such as the heat pump, can help solve the mismatch of the heat-to-power ratio between the demand and supply sides. These conclusions can guide the urban energy planning and system construction. [ABSTRACT FROM AUTHOR]

The location of temporary facilities in a construction project and the entire site layout plan directly affect project objectives such as time, labor cost, and material transportation and handling. The layout of construction sites also affects entrainment factors such as energy consumption, carbon footprints, and overall construction operation productivity. While site layout planning has been intensively investigated from a project objectives perspective, there have been very few studies of energy-efficiency-based planning, or of the sustainability performance of site layouts. This study developed a green performance evaluation system aimed at improving the sustainability of construction site layouts. The identified factors include six sustainable evaluation categories covering energy conservation and environmental protection, people-oriented principles, construction efficiency, intensity of economic growth, intensity of space use, and the overall control of process. An analytic hierarchy process (AHP) was adopted to determine the weight of each attribute and a fuzzy comprehensive evaluation method was established to carry out the evaluation. The 23 attributes adopted in this paper were identified in the literature; however, the major contribution of this paper is the development of a green performance evaluation system. This system integrates both qualitative and quantitative attributes and provides an overall evaluation of the environmental effectiveness of a construction site layout. The proposed evaluation system was validated with a commercial building project. The average utilization ratio of the case study site was calculated as 94%, and lessons learned are discussed in this paper. The case study analysis identified available site spaces around the building and examined how the arrangement of resources and facilities ensures effective connection between construction activities. The findings showed that the facility's layout plays a crucial role in energy consumption and green performance. The proposed system will support construction project managers to create high-performance construction site layouts in more scientific and systematic ways. [ABSTRACT FROM AUTHOR]

The reduction of energy consumption in the residential sector presents substantial potential through the implementation of energy efficiency improvement measures. Current trends involve the use of simulation tools which obtain the buildings' energy performance to support the development of possible solutions to help reduce energy consumption. However, simulation tools demand considerable amounts of data regarding the buildings' geometry, construction, and frequency of use. Additionally, the measured values tend to be different from the estimated values obtained with the use of energy simulation programs, an issue known as the 'performance gap'. The proposed methodology provides a solution for both of the aforementioned problems, since the amount of data needed is considerably reduced and the results are calibrated using measured values. This new approach allows to find an optimal retrofitting project by life cycle energy assessment, in terms of cost and energy savings, for individual buildings as well as several blocks of buildings. Furthermore, the potential for implementation of the methodology is proven by obtaining a comprehensive energy rehabilitation plan for a residential building. The developed methodology provides highly accurate estimates of energy savings, directly linked to the buildings' real energy needs, reducing the difference between the consumption measured and the predictions. [ABSTRACT FROM AUTHOR]