Machine Learning based Wind Power Forecasting for Operational Decision Support

Abstract

Utilizing renewable energy efficiently to meet the needs of mankind's living demands has become an extremely hot topic since global warming is the most serious global environmental problem that human beings are facing today. Burning of fossil fuels, such as coal and oil directly for generating electricity leads to environment pollution and exacerbates global warming.

This research is related to machine learning (ML) applications in wind power forecasting (WPF). The objective is to improve understanding of how artificial intelligence (AI) methods could potentially be used to improve the accuracy of WPF. A pilot conceptual system combining meteorological information and operations management has been formulated as a framework named Meteorological Information Service Decision Support System. This system consists of a meteorological information module, wind power prediction module and operations management module. This conceptual framework has been verified by quantitative analysis in empirical cases. This system has a potential to utilize meteorological information for decision-making based on condition-based maintenance in operations and management for the purpose of optimizing energy management. It aims to analyze and predict the variation of wind power for the next day or the following week to develop scheduling planning services for wind power enterprises (WPEs) based on predicting wind speed for every six hours, which is short-term wind speed prediction, through training, validating, and testing dataset.

Accurate prediction of wind speed is crucial for weather forecasting service and WPF. This study presents a carefully designed wind speed prediction model which combines fully-connected neural network (FCNN), long short-term memory (LSTM) algorithm with eXtreme Gradient Boosting (XGBoost) technique, to predict wind speed. The performance of each model is tested by using reanalysis data from European Center for Medium-Range Weather Forecasts (ECMWF) for Meteorological observatory located in Vaasa in Finland. The results show that XGBoost algorithm has similar improved prediction performance as LSTM algorithm based on root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2) compared to the commonly used traditional FCNN model. On the other hand, the XGBoost algorithm has a significant advantage on training time in this case study. Additionally, this sensitivity analysis indicates great potential of the optimized deep learning (DL) method, which is a subset of ML, in improving local weather forecast on the coding platform of Python.

The results indicate that, by using Meteorological Information Service Decision Support System, it is possible to support effective decision-making and create timely actions within the WPEs. The main outcome of this research can support decision optimization for an ML based decision support system. As a conclusion, the proposed system is very promising for potential applications in wind (power) energy management.