  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.
