Evidential Extreme Learning Machine Algorithm-Based Day-Ahead Photovoltaic Power Forecasting

Author:

Wang Minli,Wang Peihong,Zhang Tao

Abstract

The gradually increased penetration of photovoltaic (PV) power into electric power systems brings an urgent requirement for accurate and stable PV power forecasting methods. The existing forecasting methods are built to explore the function between weather data and power generation, which ignore the uncertainty of historical PV power. To manage the uncertainty in the forecasting process, a novel ensemble method, named the evidential extreme learning machine (EELM) algorithm, for deterministic and probabilistic PV power forecasting based on the extreme learning machine (ELM) and evidential regression, is proposed in this paper. The proposed EELM algorithm builds ELM models for each neighbor in the k-nearest neighbors initially, and subsequently integrates multiple models through an evidential discounting and combination process. The results can be accessed through forecasting outcomes from corresponding models of nearest neighbors and the mass function determined by the distance between the predicted point and neighbors. The proposed EELM algorithm is verified with the real data series of a rooftop PV plant in Macau. The deterministic forecasting results demonstrate that the proposed EELM algorithm exhibits 15.45% lower nRMSE than ELM. In addition, the forecasting prediction intervals obtain better performance in PICP and CWC than normal distribution.

Funder

National Natural Science Foundation of China

Publisher

MDPI AG

Subject

Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous)

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