Distributionally robust service restoration for integrated electricity-heating systems considering secondary strikes of subsequent random events DOI

Yumian Lin,

Houbo Xiong, Yue Zhou

et al.

Applied Energy, Journal Year: 2024, Volume and Issue: 380, P. 125038 - 125038

Published: Dec. 12, 2024

Language: Английский

Dynamic simulation and optimal design of a combined cold and power system with 10MW compressed air energy storage and integrated refrigeration DOI
Wei Chen,

Chengliang Qin,

Zhe Ma

et al.

Energy, Journal Year: 2024, Volume and Issue: unknown, P. 133182 - 133182

Published: Sept. 1, 2024

Language: Английский

Citations

3
A comprehensive evaluation framework for building energy systems considering economic efficiency, independence, and building–grid interaction performance indicators DOI
Yue Lu, Jide Niu, Zhe Tian

et al.

Energy and Buildings, Journal Year: 2024, Volume and Issue: 318, P. 114414 - 114414

Published: June 15, 2024

Language: Английский

Citations

1
The Cost-Optimal Control of Building Air Conditioner Loads Based on Machine Learning: A Case Study of an Office Building in Nanjing DOI Creative Commons
Zhichen Eden Guo, Xinyu Wang, Yao Wang

et al.

Buildings, Journal Year: 2024, Volume and Issue: 14(10), P. 3040 - 3040

Published: Sept. 24, 2024

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

Language: Английский

Citations

0
Distributionally robust service restoration for integrated electricity-heating systems considering secondary strikes of subsequent random events DOI

Yumian Lin,

Houbo Xiong, Yue Zhou

et al.

Applied Energy, Journal Year: 2024, Volume and Issue: 380, P. 125038 - 125038

Published: Dec. 12, 2024

Language: Английский

Citations

0