Inferential Statistics and Machine Learning Models for Short-Term Wind Power Forecasting

Ming Zhang,Hongbo Li,Xing Deng

doi:10.32604/ee.2022.017916

Abstract

The inherent randomness, intermittence and volatility of wind power generation compromise the quality of the wind power system, resulting in uncertainty in the system's optimal scheduling. As a result, it's critical to improve power quality and assure real-time power grid scheduling and grid-connected wind farm operation. Inferred statistics are utilized in this research to infer general features based on the selected information, confirming that there are differences between two forecasting categories: Forecast Category 1 (0–11 h ahead) and Forecast Category 2 (12–23 h ahead). In z-tests, the null hypothesis provides the corresponding quantitative findings. To verify the final performance of the prediction findings, five benchmark methodologies are used: Persistence model, LMNN (Multilayer Perceptron with LM learning methods), NARX (Nonlinear autoregressive exogenous neural network model), LMRNN (RNNs with LM training methods) and LSTM (Long short-term memory neural network). Experiments using a real dataset show that the LSTM network has the highest forecasting accuracy when compared to other benchmark approaches including persistence model, LMNN, NARX network, and LMRNN, and the 23-steps forecasting accuracy has improved by 19.61%.

Highlights

Coal, petroleum and gas, among other non-renewable resources, will significantly contaminate the human living environment
Wind energy has gotten a lot of attention as a renewable, inexhaustible, and unlimited free energy source
It is simple to see that there’s a significant correlation between wind speed and output power, with a coefficient of 0.96, fitting the high correlation’s range of 0.9–1.0, notably the (a) and (b) of Fig. 6, which show that the coefficient of wind power and wind speed are the highest

Summary

Introduction

Petroleum and gas, among other non-renewable resources, will significantly contaminate the human living environment. Accurate wind speed prediction can benefit the quality of energy, and real-time power grid scheduling and wind farm grid-connected operation [10,11,12,13,14]. Physical models and statistical methods are the mainly two kinds of short-term forecasting methodologies. The former method necessitates a great deal of physical data about the wind turbine, but the later usually treated as a soft computing method, is more adaptable and simple to apply in practice. The following issues are what we want to address in this paper based on the above discussion: 1) The methods to select the variables among the many available meteorological variables which has a substantial impact on the output power prediction accuracy, should be considered.

Proposed Approaches for Wind Power Forecasting

Experiments

Inferential Statistics and Performance Evaluation

Methods

Findings

Conclusion