2015/08/11 by Yübo Wang, Yubo Wang, Wang, Yubo +12
Computer Science · Engineering · Mathematics · #Applications (stat.AP) #Energy Load and Power Forecasting #FOS: Computer and information sciences #Smart Grid Energy Management #Solar Radiation and Photovoltaics #stat.AP
paper · pdf · doi:10.48550/arxiv.1508.02669
arxiv created 2015/08/11 · openalex publication_date 2015/08/11 · arxiv updated 2015/08/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper considers a typical solar installations scenario with limited sensing resources. In the literature, there exist either day-ahead solar generation prediction methods with limited accuracy, or high accuracy short timescale methods that are not suitable for applications requiring longer term prediction. We propose a two-tier (global-tier and local-tier) prediction method to improve accuracy for long term (24 hour) solar generation prediction using only the historical power data. In global-tier, we examine two popular heuristic methods: weighted k-Nearest Neighbors (k-NN) and Neural Network (NN). In local-tier, the global-tier results are adaptively updated using real-time analytical residual analysis. The proposed method is validated using the UCLA Microgrid with 35kW of solar generation capacity. Experimental results show that the proposed two-tier prediction method achieves higher accuracy compared to day-ahead predictions while providing the same prediction length. The difference in the overall prediction performance using either weighted k-NN based or NN based in the global-tier are carefully discussed and reasoned. Case studies with a typical sunny day and a cloudy day are carried out to demonstrate the effectiveness of the proposed two-tier predictions.