Development of deep learning method for predicting DC power based on renewable solar energy and multi-parameters function

Neural Computing and Applications, Journal Year: 2023, Volume and Issue: 35(21), P. 15273 - 15294

Published: April 8, 2023

Language: Английский

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Applications of artificial intelligence for energy efficiency throughout the building lifecycle: An overview

Energy and Buildings, Journal Year: 2024, Volume and Issue: 305, P. 113903 - 113903

Published: Jan. 11, 2024

Language: Английский

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Energy Forecasting: A Comprehensive Review of Techniques and Technologies

Energies, Journal Year: 2024, Volume and Issue: 17(7), P. 1662 - 1662

Published: March 30, 2024

Distribution System Operators (DSOs) and Aggregators benefit from novel energy forecasting (EF) approaches. Improved accuracy may make it easier to deal with imbalances between generation consumption. It also helps operations such as Demand Response Management (DRM) in Smart Grid (SG) architectures. For utilities, companies, consumers manage resources effectively educated decisions about consumption, EF is essential. many applications, Energy Load Forecasting (ELF), Generation (EGF), grid stability, accurate crucial. The state of the art examined this literature review, emphasising cutting-edge techniques technologies their significance for industry. gives an overview statistical, Machine Learning (ML)-based, Deep (DL)-based methods ensembles that form basis EF. Various time-series are explored, including sequence-to-sequence, recursive, direct forecasting. Furthermore, evaluation criteria reported, namely, relative absolute metrics Mean Absolute Error (MAE), Root Square (RMSE), Percentage (MAPE), Coefficient Determination (R2), Variation (CVRMSE), well Execution Time (ET), which used gauge prediction accuracy. Finally, overall step-by-step standard methodology often utilised problems presented.

Language: Английский

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A novel methodology to explain and evaluate data-driven building energy performance models based on interpretable machine learning

Applied Energy, Journal Year: 2018, Volume and Issue: 235, P. 1551 - 1560

Published: Nov. 27, 2018

Language: Английский

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Data-driven estimation of building energy consumption with multi-source heterogeneous data

Applied Energy, Journal Year: 2020, Volume and Issue: 268, P. 114965 - 114965

Published: April 18, 2020

Language: Английский

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