Fast Univariate Time Series Prediction of Solar Power for Real-Time Control of Energy Storage System

Mostafa Majidpour, Hamidreza Nazaripouya, Peter Chu, Hemanshu R. Pota and Rajit Gadh
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Mostafa Majidpour: Smart Grid Energy Research Center, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA
Hamidreza Nazaripouya: Winston Chung Global Energy Center, University of California, Riverside (UCR), Riverside, CA 92507, USA
Peter Chu: Smart Grid Energy Research Center, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA
Hemanshu R. Pota: School of Engineering & Information Technology, University of NSW, Canberra, ACT 2610, Australia
Rajit Gadh: Smart Grid Energy Research Center, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA

Forecasting, 2018, vol. 1, issue 1, 1-14

Abstract: In this paper, super-short-term prediction of solar power generation for applications in dynamic control of energy system has been investigated. In order to follow and satisfy the dynamics of the controller, the deployed prediction method should have a fast response time. To this end, this paper proposes fast prediction methods to provide the control system with one step ahead of solar power generation. The proposed methods are based on univariate time series prediction. That is, instead of using external data such as the weather forecast as the input of prediction algorithms, they solely rely on past values of solar power data, hence lowering the volume and acquisition time of input data. In addition, the selected algorithms are able to generate the forecast output in less than a second. The proposed methods in this paper are grounded on four well-known prediction algorithms including Autoregressive Integrated Moving Average (ARIMA), K-Nearest Neighbors (kNN), Support Vector Regression (SVR), and Random Forest (RF). The speed and accuracy of the proposed algorithms have been compared based on two different error measures, Mean Absolute Error (MAE) and Symmetric Mean Absolute Percentage Error (SMAPE). Real world data collected from the PV installation at the University of California, Riverside (UCR) are used for prediction purposes. The results show that kNN and RF have better predicting performance with respect to SMAPE and MAE criteria.

Keywords: solar power; machine learning; time series; forecasting (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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