Automated machine learning-based framework of heating and cooling load prediction for quick residential building design

Energy, Journal Year: 2023, Volume and Issue: 274, P. 127334 - 127334

Published: March 25, 2023

Language: Английский

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A physical model with meteorological forecasting for hourly rooftop photovoltaic power prediction

Journal of Building Engineering, Journal Year: 2023, Volume and Issue: 75, P. 106997 - 106997

Published: June 1, 2023

Language: Английский

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An improved attention-based deep learning approach for robust cooling load prediction: Public building cases under diverse occupancy schedules

Sustainable Cities and Society, Journal Year: 2023, Volume and Issue: 96, P. 104679 - 104679

Published: May 28, 2023

Space cooling in buildings is responsible for massive energy consumption and carbon emissions. Accurate load prediction can facilitate the implementation of energy-efficiency control strategies practice. In this paper, an improved attention-based deep learning approach proposed robust ultra-short-term prediction. First, a novel time representation introduced to extract periodicity non-periodicity loads efficiently. Then, long short-term memory with attention mechanism extracts properly steps by identifying relevant hidden states learns high-level temporal dependency. The additionally incorporates extreme gradient boosting through error reciprocal method, enhancing elimination errors improving robustness. study takes Guangzhou as example generates using diverse occupancy schedules five building types based on Chinese National Standard Typical Meteorological Year data. evaluated datasets comprising loads, meteorological data, contextual information. Through results analysis, outperforms other models terms accuracy robustness across all types. Additionally, model interpretation provided regarding feature importance matrixes, which enhances understanding transparency final from approach.

Language: Английский

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Ranking building design and operation parameters for residential heating demand forecasting with machine learning

Journal of Building Engineering, Journal Year: 2024, Volume and Issue: 86, P. 108817 - 108817

Published: Feb. 19, 2024

The European Union's Energy Performance in Buildings Directive has made significant strides enhancing building energy efficiency since its inception 2002. However, approximately 75% of EU buildings still fall short energy-efficient standards. Furthermore, there is a growing momentum to extend the concept nearly zero-energy entire districts, thereby fostering Net-Zero Districts. This underscores necessity for large-scale urban modelling identify and improve underperforming transition planning. Given increasing interest black box models performance, this study aims common input variables demand literature, analyse their influence, develop heating prediction model using different algorithms: Random Forest, XGBoost, Extra Trees. Four large datasets generated from white-box simulation three Spanish cities were used training testing models. features consistently stand out as most important prediction: shape factor, infiltration rate, south equivalent surface, internal gains, regardless algorithm or climatic zone. multi-location XGBoost with an optimizer emerged best-performing model, average Mean Absolute Percentage Error value hovering around 40%. Analysis employing SHapley Additive exPlanation (SHAP) values showcases model's ability factors that drive higher demand, alongside strong predictive performance. suggests potential integration into programmes key be addressed during renovation. Additionally, results show XGBoost-based software's identifying renovation targets.

Language: Английский

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Optimizing building energy performance predictions: A comparative study of artificial intelligence models

Journal of Building Engineering, Journal Year: 2024, Volume and Issue: 88, P. 109247 - 109247

Published: April 4, 2024

Language: Английский

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Automated data-driven building energy load prediction method based on generative pre-trained transformers (GPT)

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 134824 - 134824

Published: Feb. 1, 2025

Language: Английский

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Energy Management in Modern Buildings Based on Demand Prediction and Machine Learning—A Review

Energies, Journal Year: 2024, Volume and Issue: 17(3), P. 555 - 555

Published: Jan. 23, 2024

Increasing building energy consumption has led to environmental and economic issues. Energy demand prediction (DP) aims reduce use. Machine learning (ML) methods have been used improve consumption, but not all performed well in terms of accuracy efficiency. In this paper, these are examined evaluated for modern (MB) DP.

Language: Английский

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Towards a net-zero-energy building with smart control of Trombe walls, underground air ducts, and optimal microgrid composed of renewable energy systems

Energy, Journal Year: 2024, Volume and Issue: 294, P. 130703 - 130703

Published: Feb. 16, 2024

Language: Английский

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Short-term PV power data prediction based on improved FCM with WTEEMD and adaptive weather weights

Journal of Building Engineering, Journal Year: 2024, Volume and Issue: 90, P. 109408 - 109408

Published: April 21, 2024

Language: Английский

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An ensemble learning model for estimating the virtual energy storage capacity of aggregated air-conditioners

Journal of Energy Storage, Journal Year: 2023, Volume and Issue: 59, P. 106512 - 106512

Published: Jan. 3, 2023

Language: Английский

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