Impact of data for forecasting on performance of model predictive control in buildings with smart energy storage

Langtry, Max; Wichitwechkarn, Vijja; Ward, Rebecca; Zhuang, Chaoqun; Kreitmair, Monika J.; Makasis, Nikolas; Xuereb Conti, Zack; Choudhary, Ruchi

doi:10.1016/j.enbuild.2024.114605

Energy and Buildings

2024

DOI: 10.1016/j.enbuild.2024.114605

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Impact of data for forecasting on performance of model predictive control in buildings with smart energy storage

Max Langtry,

Vijja Wichitwechkarn,

Rebecca Ward

et al.

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2024

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Cited by 3 publications

References 53 publications

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CityLearn v2: energy-flexible, resilient, occupant-centric, and carbon-aware management of grid-interactive communities

Nweye,

Kaspar,

Buscemi

et al. 2024

Journal of Building Performance Simulation

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CityLearn v2: energy-flexible, resilient, occupant-centric, and carbon-aware management of grid-interactive communities

Nweye,

Kaspar,

Buscemi

et al. 2024

Journal of Building Performance Simulation

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Rationalising data collection for supporting decision making in building energy systems using value of information analysis

Langtry,

Zhuang,

Ward

et al. 2024

Journal of Building Performance Simulation

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The Cost-Optimal Control of Building Air Conditioner Loads Based on Machine Learning: A Case Study of an Office Building in Nanjing

Guo,

Wang,

Wang

et al. 2024

Buildings

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Building envelopes and indoor environments exhibit thermal inertia, forming a virtual energy storage system in conjunction with the building air conditioner (AC) system. This system represents a current demand response resource for building electricity use. Thus, this study centers on the CatBoost algorithm within machine learning (ML) technology, utilizing the LASSO regression model for feature selection and applying the Optuna framework for hyperparameter optimization (HPO) to develop a cost-optimal control method for minimizing building AC loads. This method addresses the challenges associated with traditional load forecasting and control methods, which are often impacted by environmental temperature, building parameters, and user behavior uncertainties. These methods struggle to accurately capture the complex dynamics and nonlinear relationships of AC operations, making it difficult to devise AC operation and virtual energy storage scheduling strategies effectively. The proposed method was applied and validated using a case study of an office building in Nanjing, China. The prediction results showed coefficient of variation in root mean square error (CV-RMSE) values of 6.4% and 2.2%. Compared with the original operating conditions, the indoor temperature remained within a comfortable range, the AC load was reduced by 5.25%, and the operating energy costs were reduced by 24.94%. These results demonstrate that the proposed method offers improved computational efficiency, enhanced model performance, and economic benefits.

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Impact of data for forecasting on performance of model predictive control in buildings with smart energy storage

Cited by 3 publications

References 53 publications

CityLearn v2: energy-flexible, resilient, occupant-centric, and carbon-aware management of grid-interactive communities

CityLearn v2: energy-flexible, resilient, occupant-centric, and carbon-aware management of grid-interactive communities

Rationalising data collection for supporting decision making in building energy systems using value of information analysis

The Cost-Optimal Control of Building Air Conditioner Loads Based on Machine Learning: A Case Study of an Office Building in Nanjing

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