Photovoltaic Power Forecasting by Evolutionary Algorithm-Based Improved Extreme Learning Machine

Abstract

In order to improve the grid energy management capability in a smart grid environment, this work aims to apply modified extreme learning machine (ELM) forecasting method for short-term prediction of PV power and overcomes some of the major challenges during on-grid mode of operation. Solar power generation is irregular and changeable in nature. The solar source is highly reliant on temperature and other atmospheric factors. Integration of solar plant with conventional grid brings the smart grid concept to the power engineering space. Now the major challenge to grid supervision is accurate forecasting of photovoltaic power in a smart grid with less cost. Thus, a modified extreme learning machine forecasting model is designed for short-term prediction of solar power. Further to improve the accuracy and stability of the system, the weights of ELM technique are optimized by runner and root optimization technique. Results from the current study depict that the proposed method performs better by providing a smaller forecasting error than other methods. The modified extreme learning machine is applied to a real-time data set of a real-time solar power plant, located at the roof top of an academic building, situated at Bhubaneswar, Odisha, India, whose geographical location is given in Table 1. Also a new optimization technique is applied for its weight optimization which minimizes the forecasting error.