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Journal of Renewable Energy
Volume 2017, Article ID 2437387, 7 pages
https://doi.org/10.1155/2017/2437387
Research Article

Feature Selection and ANN Solar Power Prediction

1Department of Computer Science, City College of San Francisco (CCSF), Mailbox LB8, 50 Phelan Ave., San Francisco, CA 94112, USA
2Jack Baskin School of Engineering, University of California, Santa Cruz, 1156 High Street, Mail Stop SOE2, Santa Cruz, CA 95064, USA

Correspondence should be addressed to Joel Kubby; ude.cscu.eos@ybbukj

Received 8 May 2017; Revised 14 September 2017; Accepted 16 October 2017; Published 8 November 2017

Academic Editor: Ben Xu

Copyright © 2017 Daniel O’Leary and Joel Kubby. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

A novel method of solar power forecasting for individuals and small businesses is developed in this paper based on machine learning, image processing, and acoustic classification techniques. Increases in the production of solar power at the consumer level require automated forecasting systems to minimize loss, cost, and environmental impact for homes and businesses that produce and consume power (prosumers). These new participants in the energy market, prosumers, require new artificial neural network (ANN) performance tuning techniques to create accurate ANN forecasts. Input masking, an ANN tuning technique developed for acoustic signal classification and image edge detection, is applied to prosumer solar data to improve prosumer forecast accuracy over traditional macrogrid ANN performance tuning techniques. ANN inputs tailor time-of-day masking based on error clustering in the time domain. Results show an improvement in prediction to target correlation, the value, lowering inaccuracy of sample predictions by 14.4%, with corresponding drops in mean average error of 5.37% and root mean squared error of 6.83%.