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arXiv · 1508.02669

Two-Tier Prediction of Solar Power Generation with Limited Sensing Resource

Yubo Wang·Bin Wang·Rui Huang·Chi-Cheng Chu·Hemanshu R. Pota·Rajit Gadh
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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.

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Yubo Wang, Bin Wang, Rui Huang, Chi-Cheng Chu, Hemanshu R. Pota, Rajit Gadh. 2015-08-11. Two-Tier Prediction of Solar Power Generation with Limited Sensing Resource. https://arxiv.org/abs/1508.02669

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