An Edge Computing-oriented Net Power Forecasting for PV-assisted Charging Station: Model Complexity and Forecasting Accuracy Trade-off. (15th March 2022)

• Record Type: Journal Article
• Authors: Shi, Jiaqi; Liu, Nian; Huang, Yujing; Ma, Liya
• Is Part Of: Applied energy. Volume 310(2022)
• Publication Date: 2022-03-15
• Subjects: PV-assisted charging station (PVCS) -- Net power forecasting -- Edge computing -- Online training -- Model complexity; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2021.118456
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: The property of actual PVCS is investigated by characterizing the EV charging behaviors and PV power distribution to explore the net power data regularity. The external factors that most correlated with PVCS power variation are captured by interpretable algorithms to guide EV behaviors to maximally utilize renewable energy. At the software algorithm design stage, the forecasting performance trade-off between algorithm complexity and forecasting accuracy is fully considered for edge computing use. The model complexity is maximumly reduced by covering the entire cycle of forecasting process, including input data pruning, lightweight model training, and hyperparameter optimization. At the hardware configuration stage, forecasting programming is implemented on Raspberry pi-based edge platform for rapid online training. The forecasting model is tested on various scenarios to verify the algorithm feasibility on the edge device. Abstract: The PV-assisted charging station (PVCS) aggregates the two key resources of electric vehicle (EV) charging load and photovoltaic (PV) system to maximize operation profit. In order to quantify the PVCS impact to grid and the renewable energy utilization by EV charging load, it is crucial to predict the PVCS net power and capture the most correlated factors for power variation. Given that the PVCS is located near the user side, the forecasting model complexity must be restricted to meet the online training demands on edge computing, apart from ensuring the prediction accuracy. A PVCS net power forecasting approach is proposed by simplifying model complexity from the entire cycle of training process, including the input data pruning, lightweight model training and hyperparameter optimization. Finally, a comprehensive analysis of the real data in PVCS shows that the most sensitivity factors affecting the net power of PVCS is time of use price, and the deep auto-encoded extreme learning machine (DA-ELM) can make a satisfactory compromise between prediction accuracy and model complexity for edge computing utilization. The forecasting model has outstanding prediction performance on Raspberry pi-based edge platform, which has enough significance for PVCS promotion.