A review of machine learning in building load prediction

Zhang, Liang; Wen, Jin; Li, Yanfei; Chen, Jianli; Ye, Yunyang; Fu, Yangyang; Livingood, William

doi:10.1016/j.apenergy.2021.116452

Cited by 374 publications

(126 citation statements)

References 187 publications

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“…ML offers several opportunities for problems related to monitoring of operations and optimisation. However, it has become increasingly clear that advances in DH-related ML research have a disproportionate focus on some problem areas, specifically forecasting [15][16][17][18], while missing out on several interesting problems, e.g., substation heat transfer, occupancy comfort, etc.…”

Section: Motivation and Objectivementioning

confidence: 99%

“…These opportunities can enable DH systems to satisfy the demands of today's society, e.g., supplying DH at low temperatures while optimising the indoor climate. Improving building energy performance, reducing costs, satisfying regulatory constraints, meeting sustainability goals, etc., are some of the challenges addressed by published ML research [15,25]. A synthesis of how ML has been applied in DH begins in this section with an overview of previous review studies to highlight current knowledge, knowledge gaps and possible future directions.…”

Section: Machine Learning Applied To District Heatingmentioning

confidence: 99%

“…To understand existing literature reviews in this area, we provide an overview of the knowledge gaps identified in various studies, the types of ML models reported, datasets employed and building characteristics (features) studied. Most research articles on the application of ML in DH have occurred in the last 10 years [7,15,18]. Each of the review studies addressed different research gaps, considered research articles over different time periods and considered various building types.…”

Section: Highlights Of Existing Literature Reviewsmentioning

confidence: 99%

“…Some choose to cover the full breadth of ML methods, while others limit the scope of the ML models included [7,26]. Knowledge gaps addressed by previous review studies include: (1) lack of an overview of building performance models, in general [7,26], and the lack of data driven approaches for building performance, in particular [15,25], (2) lack of comprehensive studies of ML in urban building energy performance [7], (3) lack of ML performance and accuracy assessment on DH problems [16] and ( 4) lack of open datasets for benchmarking algorithms [15]. Other review studies highlight the need to compare the forecasting performance of common ML algorithms such as Artificial Neural Networks (ANN), Support Vector Machine (SVM) or Gaussian-Based Regression (GBR) [7,18].…”

Section: Highlights Of Existing Literature Reviewsmentioning

confidence: 99%

“…To improve prediction accuracy, there is increasing interest in using hybrid methods, e.g., ANN and SVM [8,15]. None of the reviews found a single overall best model although SVM was reported to perform relatively better compared to ANN and regression-based methods, yet the most used method for building energy prediction remains ANN [16].…”

Section: Highlights Of Existing Literature Reviewsmentioning

confidence: 99%

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Opportunities for Machine Learning in District Heating

et al. 2021

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The district heating (DH) industry is facing an important transformation towards more efficient networks that utilise significantly lower water temperatures to distribute the heat. This change requires taking advantage of new technologies, and Machine Learning (ML) is a popular direction. In the last decade, we have witnessed an extreme growth in the number of published research papers that focus on applying ML techniques to the DH domain. However, based on our experience in the field, and an extensive review of the state-of-the-art, we perceive a mismatch between the most popular research directions, such as forecasting, and the challenges faced by the DH industry. In this work, we present our findings, explain and demonstrate the key gaps between the two communities and suggest a road-map ahead towards increasing the impact of ML research in the DH industry.

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Section: Motivation and Objectivementioning

confidence: 99%

Section: Machine Learning Applied To District Heatingmentioning

confidence: 99%

Section: Highlights Of Existing Literature Reviewsmentioning

confidence: 99%

Section: Highlights Of Existing Literature Reviewsmentioning

confidence: 99%

Section: Highlights Of Existing Literature Reviewsmentioning

confidence: 99%

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Opportunities for Machine Learning in District Heating

et al. 2021

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Investigation of RBFNN Based on Improved PSO Optimization Algorithm for Performance and Emissions Prediction of a High‐Pressure Common‐Rail Diesel Engine

Wang

Yao

et al. 2023

Advcd Theory and Sims

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The purpose of this study is improve calibration efficiency and obtain accurate diesel engine operating parameters, achieving improved diesel engine emissions and fuel efficiency. A PSO‐RBF (particle swarm optimization‐radial basis function) diesel engine performance prediction model combining an improved PSO (particle swarm optimization algorithm and an RBF neural network is proposed. A space‐filling experimental design method for diesel engine performance prediction is proposed based on the actual operating conditions of diesel engines. Training data are collected at the bench to build the RBF prediction model. An optimization PSO search method is proposed to improve the PSO optimization capability. An improved PSO algorithm is used to optimize the model and improve prediction accuracy. Then the BSFC (diesel brake‐specific fuel consumption), NOx ((Nitrogen Oxid), CO (Carbon Monoxide), and HC (Hydrocarbon) prediction models are constructed. Results show that the PSO‐RBF can find the global solution with good prediction accuracy and generalization ability during small amounts of data. The PSO‐RBF model fitting degrees of BSFC, NOx, CO, and HC are 0.9952, 0.9910, 0.9820, and 0.9870 respectively. Mean relative errors are 3.02%, 2.78%, 1.39%, and 2.01% respectively. Mean absolute percentage errors are 1.58%, 3.26%, 3.69%, and 2.96% respectively. The optimized model R2 (Model determination coefficient) is improved by 0.065, 0.102, 0.10, and 0.085, respectively.

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Review of Uncertainties in Building Characterization for Urban-Scale Energy Modeling

Bolluk

Seyis

2022

Advances in Building Information Modeling

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A review of machine learning in building load prediction

Cited by 374 publications

References 187 publications

Opportunities for Machine Learning in District Heating

Opportunities for Machine Learning in District Heating

Investigation of RBFNN Based on Improved PSO Optimization Algorithm for Performance and Emissions Prediction of a High‐Pressure Common‐Rail Diesel Engine

Review of Uncertainties in Building Characterization for Urban-Scale Energy Modeling

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