Influences in Forecast Errors for Wind and Photovoltaic Power: A Study on Machine Learning Models
This work addresses the need for more reliable planning in electric power distribution by providing detailed insights into forecast uncertainties, though it is incremental as it builds on existing ML methods.
The study analyzed factors affecting uncertainty in wind and photovoltaic power forecasts using four machine learning models, finding substantial differences in uncertainty based on models, data coverage, and seasonal patterns.
Despite the increasing importance of forecasts of renewable energy, current planning studies only address a general estimate of the forecast quality to be expected and selected forecast horizons. However, these estimates allow only a limited and highly uncertain use in the planning of electric power distribution. More reliable planning processes require considerably more information about future forecast quality. In this article, we present an in-depth analysis and comparison of influencing factors regarding uncertainty in wind and photovoltaic power forecasts, based on four different machine learning (ML) models. In our analysis, we found substantial differences in uncertainty depending on ML models, data coverage, and seasonal patterns that have to be considered in future planning studies.