Guest Editorial for the Special Section on Advances in Renewable Energy Forecasting: Predictability, Business Models and Applications in the Power Industry

The papers in this special section focus on advances in renewable energy forecasting, predictability, business models, and applications in the power industry. During the last 25 years, research has been conducted for developing renewable energy source (RES) forecasting algorithms, especially for wind and solar energy, seeking an improvement of predictability and uncertainty forecasting products. Research on wave energy forecasting is also being conducted, although this technology is not at the same maturity levels of wind and solar energy technologies. Furthermore, the number of companies selling forecasting services has proliferated and the reliability and availability of the services have improved. Currently, power system operators and electrical energy traders use weather and power forecasts embedded in their decision-making processes. Despite all this research and adoption by the energy industry, deterministic forecasts are still predominant in utility practice mainly due to: i) lack of understanding and standardization of uncertainty forecast products; and ii) high computational time associated with stochastic and robust optimization approaches. Moreover, proven business cases are also needed to demonstrate the benefits of uncertainty forecasts to end-users.

Automated summary

The papers in this special section discuss the advances in renewable energy forecasting, predictability, business models, and applications in the power industry. While there has been significant research and adoption of these technologies in the energy industry, deterministic forecasts are still more commonly used due to a lack of understanding and standardization of uncertainty forecast products and the longer computational time associated with stochastic and robust optimization approaches. Additionally, proven business cases are needed to demonstrate the benefits of uncertainty forecasts to end-users.