Two-tier prediction of solar power generation with limited sensing resource

Abstract

Abstract-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.

Forecasting Short-Term Solar PV Using Hierarchical Clustering and Cascade Model

“…The difference in the final accuracies are within 0.1%. In addition, (Wang et al, 2015) proposed a two-tier (global-tier and local-tier) prediction method to improve accuracy for long term (24 hours) solar generation prediction using only the historical power data based on DWKNN and Neural Network approach. Both models archive the results with high accuracy.…”