Accuracy Improvement of Energy Prediction for Solar-Energy-Powered Embedded Systems

• Published in: IEEE transactions on very large scale integration (VLSI) systems Vol. 24; no. 6; pp. 2062 - 2074
• Main Authors: Liu, Qiang, Zhang, Qi-Jun
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Embedded systems; Feature extraction; Knowledge base; Knowledge-based neural network (KBNN); Mathematical models; multilayer perceptron (MLP) network; Neural networks; Predictive models; Robustness; Solar energy; solar energy prediction; solar-energy-powered embedded systems; Time measurement; Training; Training data; Very large scale integration; multilayer perceptron (MLP) network; solar-energy-powered embedded systems; Knowledge-based neural network (KBNN); solar energy prediction
• ISSN: 1063-8210; 1557-9999
• DOI: 10.1109/TVLSI.2015.2497147

Solar energy prediction is a key to the power management in the electronic embedded system that operates using the harvested solar energy. This paper proposes accuracy improvement approaches for the solar energy prediction based on artificial neural networks, in order to increase the robustness of solar-energy-powered systems. Two complementary neural network models, multilayer perceptron (MLP) network and knowledge-based neural network (KBNN), are exploited to predict the future solar energy, through offline and online training. MLP is constructed under the guidance of the proposed input parameter selection approach and is used when the training data are sufficient. KBNN is employed to take advantage of the existing prediction models and is especially valuable when the training data are insufficient. Built on top of the existing prediction approaches, our work results in a synergy that can overcome the accuracy limitation of the existing prediction approaches. The experimental results show the prediction accuracy improvements by up to 65.4%, compared with the existing approaches. The results also demonstrate the capability of KBNN in providing a reliable model, especially when fewer training data are available.