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- W4366607050 abstract "Accurate cooling load forecasting and optimal control strategy for the energy management of district ice storage system (DISS) are two key factors in improving performance and achieving energy cost savings. This work constructs Similar Days (SD) Algorithm to screen and reduce the dimension of the training samples. The SD-based Support Vector Regression (SVR) model shows superiority in hourly forecasting accuracy compared with single models. Further, the Moth-Flame Optimization (MFO) is introduced for hyperparameters optimization, which improves the accuracy and greatly reduces the daily cumulative error, enabling to implement optimal control during the cooling period and to charge ice storage tank (IST) to appropriate amount after midnight. This work also proposes an innovative optimization approach based on the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), including a mathematical model of the system. The optimization acts on the cooling capacity distribution among each equipment, and the heat pump outlet temperature. Comparisons with current and conventional heuristic strategies are considered for typical-day and design-day conditions. Results indicate that the maximum hourly energy consumption under the optimal strategy during the top/peak tariff hours is 41.16% of the current strategy, and the daily electricity cost is saved by 25.03%, moreover, the lower the partial load, the more significant the reduction effect." @default.
- W4366607050 created "2023-04-23" @default.
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- W4366607050 date "2023-08-01" @default.
- W4366607050 modified "2023-10-16" @default.
- W4366607050 title "Optimal control and energy efficiency evaluation of district ice storage system" @default.
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- W4366607050 doi "https://doi.org/10.1016/j.energy.2023.127598" @default.
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