Wind Power Forecasting Using Machine Learning: State of the Art, Trends and Challenges

The future challenges in the power grid have become more real the last decade. The wind power production increases rapidly. Having compatible wind turbines in the electricity spot market, forces conventional powerplants to shut down. This affects the reserve markets whose cost increases as the wind power capacity grows. By having wind turbines participate in the reserve markets, the costs could be reduced. Wind turbines are now excluded from the Danish markets due to low reliability of forecasts. Wind power forecasts must reflect the reality if the TSOs are to rely on the availability of the wind turbines. A State-of-the-Art analysis of four machine learning methods, Neural Network, Support Vector Machine, k Nearest Neighbor and Random Forest, investigates the challenges and advantages of the algorithms within wind power forecasting. The State-of-the-Art results showed that Neural Network and Support Vector Machine are the most common algorithms within the field. By investigating the algorithms, it was found that Neural Network and Support Vector Machine have several parameters, which will increase errors, if tuned poorly. Further it was found that due to the many parameters, the algorithms can be modified to fit many specific cases. There is a growing trend in the general use of machine learning in order to digitalize wind power forecasts. A more stable, automatic, and human-error-free prediction of wind power will bring wind turbines one step closer in participating in the reserve markets.

I. Introduction

Global warming is a well-known and growing problem. Power production from conventional units are large contributors to the emission of greenhouse gasses. Utilizing renewable energy sources, instead of coal and oil-fired powerplants, have become a global trend during the last decades. Renewable Energy, RE, is fuel-free, and the production cost has decreased to a competitive level. Many countries have substituted a share of the conventional units to RE. The Northern part of Europe is placed in a wind-belt, which makes the utilization of wind turbines very profitable. Denmark has in 2019 reached a goal of more than 50% of the electricity demand being covered by RE, with wind being the largest contributor [1]. Biomass is not included in the RE part. This has lowered the emission of greenhouse gasses significantly and created a change in players on the electricity market.