Short-term building energy consumption prediction strategy based on modal decomposition and reconstruction algorithm

Jiao, Yinghao; Tan, Zhi; Zhang, De; Zheng, Q.P.

doi:10.1016/j.enbuild.2023.113074

Cited by 16 publications

(2 citation statements)

References 54 publications

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“…Consequently, building managers, urban planners, and researchers encounter difficulties in selecting the most suitable algorithm for more accurate prediction results [52]. This, in turn, impedes the creation of a cohesive strategy for energy optimization across the spectrum of buildings within a smart city [53].…”

Section: Introductionmentioning

confidence: 99%

Auto-Evaluation Model for the Prediction of Building Energy Consumption That Combines Modified Kalman Filtering and Long Short-Term Memory

Yang,

Mao

2023

Sustainability

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As the world grapples with the challenges posed by climate change and depleting energy resources, achieving sustainability in the construction and operation of buildings has become a paramount concern. The construction and operation of buildings account for a substantial portion of global energy consumption and carbon emissions. Hence, the accurate prediction of building energy consumption is indispensable for reducing energy waste, minimizing greenhouse gas emissions, and fostering sustainable urban development. The aspiration to achieve predicted outcomes with remarkable accuracy has emerged as a pivotal objective, coinciding with the burgeoning popularity of deep learning techniques. This paper presents an auto-evaluation model for building energy consumption prediction via Long Short-Term Memory with modified Kalman filtering (LSTM-MKF). Results gleaned from data validation activities evince a notable transformation—a reduction of the maximal prediction error from an initial 83% to a markedly ameliorated 24% through the intervention of the proposed model. The LSTM-MKF model, a pioneering contribution within this paper, clearly exhibits a distinct advantage over the other models in terms of predictive accuracy, as underscored by its superior performance in all three key metrics, including mean absolute error, root mean square error, and mean square error. The model presents excellent potential as a valuable tool for enhancing the precision of predictions of building energy consumption, a pivotal aspect in energy efficiency, smart city development, and the formulation of informed energy policy.

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Section: Introductionmentioning

confidence: 99%

Auto-Evaluation Model for the Prediction of Building Energy Consumption That Combines Modified Kalman Filtering and Long Short-Term Memory

Yang,

Mao

2023

Sustainability

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“…Similarly, Jiao et al [96] proposed a short-term building energy consumption prediction strategy based on random forest and CNN-GRU. Random forest predicted the high-frequency components, and CNN-GRU extracted the spatiotemporal features of the low-frequency components.…”

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confidence: 99%

A Review on Machine/Deep Learning Techniques Applied to Building Energy Simulation, Optimization and Management

Villano,

Mauro,

Pedace

2024

Thermo

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Given the climate change in recent decades and the ever-increasing energy consumption in the building sector, research is widely focused on the green revolution and ecological transition of buildings. In this regard, artificial intelligence can be a precious tool to simulate and optimize building energy performance, as shown by a plethora of recent studies. Accordingly, this paper provides a review of more than 70 articles from recent years, i.e., mostly from 2018 to 2023, about the applications of machine/deep learning (ML/DL) in forecasting the energy performance of buildings and their simulation/control/optimization. This review was conducted using the SCOPUS database with the keywords “buildings”, “energy”, “machine learning” and “deep learning” and by selecting recent papers addressing the following applications: energy design/retrofit optimization, prediction, control/management of heating/cooling systems and of renewable source systems, and/or fault detection. Notably, this paper discusses the main differences between ML and DL techniques, showing examples of their use in building energy simulation/control/optimization. The main aim is to group the most frequent ML/DL techniques used in the field of building energy performance, highlighting the potentiality and limitations of each one, both fundamental aspects for future studies. The ML approaches considered are decision trees/random forest, naive Bayes, support vector machines, the Kriging method and artificial neural networks. The DL techniques investigated are convolutional and recursive neural networks, long short-term memory and gated recurrent units. Firstly, various ML/DL techniques are explained and divided based on their methodology. Secondly, grouping by the aforementioned applications occurs. It emerges that ML is mostly used in energy efficiency issues while DL in the management of renewable source systems.

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A deep belief network-based energy consumption prediction model for water source heat pump system

Guo,

Liu,

Wang

et al. 2024

Applied Thermal Engineering

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Short-term building energy consumption prediction strategy based on modal decomposition and reconstruction algorithm

Cited by 16 publications

References 54 publications

Auto-Evaluation Model for the Prediction of Building Energy Consumption That Combines Modified Kalman Filtering and Long Short-Term Memory

Auto-Evaluation Model for the Prediction of Building Energy Consumption That Combines Modified Kalman Filtering and Long Short-Term Memory

A Review on Machine/Deep Learning Techniques Applied to Building Energy Simulation, Optimization and Management

A deep belief network-based energy consumption prediction model for water source heat pump system

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