A day-ahead planning for multi-energy system in building community

Ouyang, Tiancheng; Zhang, Mingliang; Wu, Wencong; Zhao, Jiaqi; Xu, Hui

doi:10.1016/j.energy.2022.126399

Cited by 12 publications

(5 citation statements)

References 43 publications

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“…1) Long Short-Term Memory Neural Network (Ouyang et al, 2023) used LSTM forecasting algorithm for electric cooling load forecasting (Wu et al, 2023). developed a load forecasting model based on LSTM neural network for industrial enterprises.…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

Review of multiple load forecasting method for integrated energy system

Liu,

Li,

et al. 2023

Front. Energy Res.

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In order to further improve the efficiency of energy utilization, Integrated Energy Systems (IES) connect various energy systems closer, which has become an important energy utilization mode in the process of energy transition. Because the complex and variable multiple load is an important part of the new power system, the load forecasting is of great significance for the planning, operation, control, and dispatching of the new power system. In order to timely track the latest research progress of the load forecasting method and grasp the current research hotspot and the direction of load forecasting, this paper reviews the relevant research content of the forecasting methods. Firstly, a brief overview of Integrated Energy Systems and load forecasting is provided. Secondly, traditional forecasting methods based on statistical analysis and intelligent forecasting methods based on machine learning are discussed in two directions to analyze the advantages, disadvantages, and applicability of different methods. Then, the results of Integrated Energy Systemss multiple load forecasting for the past 5 years are compiled and analyzed. Finally, the Integrated Energy Systems load forecasting is summarized and looked forward.

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Section: Recurrent Neural Networkmentioning

confidence: 99%

Review of multiple load forecasting method for integrated energy system

Liu,

Li,

et al. 2023

Front. Energy Res.

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“…However, market randomness greatly affects this method, and a comprehensive analysis of influencing factors cannot be realized [16]. From the complexity of planning data, BIM and AI technology are used to analyze semi-structured and structural data and compare the judgment results of semi-structural and non-structural factors [17], which proves that BIM and AI technologies have high accuracy in calculating influencing factors; 3) The factor analysis of planning data with ant colony algorithm and Bayesian algorithm shows that the accuracy and rationality of previous intelligent algorithms are poor, while the calculation accuracy of BIM and AI technology is high. In summary, although the intelligent algorithm can comprehensively calculate the planning data in the past, the calculation results are not satisfactory, and it cannot meet the requirements of massive and complex planning data calculation [18].…”

Section: Figure 1 Application Of Bim and Ai Technology In Architectur...mentioning

confidence: 99%

Revolutionizing the Industrialization of Buildings with BIM and AI: Factors Influencing Adoption and Development and Building Planning

Yang,

Wang,

Zheng

2023

Int. j. commun. netw. inf. secur.

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The continuous development of BIM and AI has made the analysis of building industrialization a hot spot in research and made the issue of industrial reform the focus of computer and construction research. The traditional architectural planning method cannot solve the problem of building industrialization reform under AI technology, and the accuracy of architectural planning is low. Therefore, this paper proposes a model based on BIM and AI technology to analyze the development mode and planning of building reform. First, BIM and AI technology are used to analyze the building data, and the irrelevant construction industrialization data is deleted according to the development mode and planning of the data characteristics. Then, according to the judgment results of construction data, compared with the traditional architectural planning method, the content of different construction industries is deeply excavated, and the influencing factors with higher possibilities are output. After simulation test and analysis, BIM and AI technology can improve the judgment accuracy, integrity, and rationality of the influencing factors of building industrialization planning, with an accuracy rate of 96.4%, and make the correct choice of development mode to meet the judgment needs of influencing factors of architectural planning.

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“…In ref. [20], for building community development, day‐ahead integrated PV, WTG, and natural gas planning, for both electrical and cooling demands, was suggested. The authors of ref.…”

Section: Introductionmentioning

confidence: 99%

“…The authors of ref. [20] came to the conclusion that energy mismatched percentages for the electrical and cooling demands, respectively, had fallen to 4.6% and 5% when compared to energy allocation strategy. The economic and environmental implications of renewable energy sources were covered in ref.…”

Section: Introductionmentioning

confidence: 99%

Dual‐stage day‐ahead optimized performance of renewable‐based microgrids

Helmi

Abo‐Elyousr

Alhelou

et al. 2023

IET Renewable Power Gen

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In distributed networks, wind turbine generators (WTGs) are to be optimally sized and positioned for cost‐effective and efficient network service. Various meta‐heuristic algorithms have been proposed to allocate WTGs within microgrids. However, the ability of these optimizers might not be guaranteed with uncertainty loads and wind generations. This paper presents novel meta‐heuristic optimizers to mitigate extreme voltage drops and the total costs associated with WTGs allocation within microgrids. Arithmetic optimization algorithm (AOA), coronavirus herd immunity optimizer, and chimp optimization algorithm (ChOA) are proposed to manipulate these aspects. The trialed optimizers are developed and analyzed via Matlab, and fair comparison with the grey wolf optimization, particle swarm optimization, and the mature genetic algorithm are introduced. Numerical results for a large‐scale 295‐bus system (composed of IEEE 141‐bus, IEEE 85‐bus, IEEE 69‐bus subsystems) results illustrate the AOA and the ChOA outperform the other optimizers in terms of satisfying the objective functions, convergence, and execution time. The voltage profile is substantially improved at all buses with the penetration of the WTG with satisfactory power losses through the transmission lines. Day‐ahead is considered generic and efficient in terms of total costs. The AOA records costs of 16.575M$/year with a reduction of 31% compared to particle swarm optimization.

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A day-ahead planning for multi-energy system in building community

Cited by 12 publications

References 43 publications

Review of multiple load forecasting method for integrated energy system

Review of multiple load forecasting method for integrated energy system

Revolutionizing the Industrialization of Buildings with BIM and AI: Factors Influencing Adoption and Development and Building Planning

Dual‐stage day‐ahead optimized performance of renewable‐based microgrids

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