Physical energy and data-driven models in building energy prediction: A review

Energy Reports, Journal Year: 2022, Volume and Issue: 8, P. 2656 - 2671

Published: Feb. 10, 2022

The difficulty in balancing energy supply and demand is increasing due to the growth of diversified flexible building resources, particularly rapid development intermittent renewable being added into power grid. accuracy consumption prediction top priority for electricity market management ensure grid safety reduce financial risks. speed load are fundamental prerequisites different objectives such as long-term planning short-term optimization systems buildings past few decades have seen impressive time series forecasting models focusing on domains objectives. This paper presents an in-depth review discussion models. Three widely used approaches, namely, physical (i.e., white box), data-driven black hybrid grey were classified introduced. principles, advantages, limitations, practical applications each model investigated. Based this review, research priorities future directions domain highlighted. conclusions drawn could guide prediction, therefore facilitate efficiency buildings.

Language: Английский

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Predicting property prices with machine learning algorithms

Journal of Property Research, Journal Year: 2020, Volume and Issue: 38(1), P. 48 - 70

Published: Oct. 19, 2020

This study uses three machine learning algorithms including, support vector (SVM), random forest (RF) and gradient boosting (GBM) in the appraisal of property prices. It applies these methods to examine a data sample about 40,000 housing transactions period over 18 years Hong Kong, then compares results algorithms. In terms predictive power, RF GBM have achieved better performance when compared SVM. The metrics including mean squared error (MSE), root (RMSE) absolute percentage (MAPE) associated with two also unambiguously outperform those However, our has found that SVM is still useful algorithm fitting because it can produce reasonably accurate predictions within tight time constraint. Our conclusion offers promising, alternative technique valuation research especially relation price prediction.

Language: Английский

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Artificial Intelligence Evolution in Smart Buildings for Energy Efficiency

Applied Sciences, Journal Year: 2021, Volume and Issue: 11(2), P. 763 - 763

Published: Jan. 14, 2021

The emerging concept of smart buildings, which requires the incorporation sensors and big data (BD) utilizes artificial intelligence (AI), promises to usher in a new age urban energy efficiency. By using AI technologies consumption can be reduced through better control, improved reliability, automation. This paper is an in-depth review recent studies on application (AI) buildings building management system (BMS) demand response programs (DRPs). In addition elaborating principles applications AI-based modeling approaches widely used use prediction, evaluation framework introduced for assessing research conducted this field across major domains, including energy, comfort, design, maintenance. Finally, includes discussion open challenges future directions buildings.

Language: Английский

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A novel improved model for building energy consumption prediction based on model integration

Applied Energy, Journal Year: 2020, Volume and Issue: 262, P. 114561 - 114561

Published: Feb. 8, 2020

Language: Английский

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Recurrent inception convolution neural network for multi short-term load forecasting

Energy and Buildings, Journal Year: 2019, Volume and Issue: 194, P. 328 - 341

Published: April 24, 2019

Language: Английский

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