Machine learning for wind and solar PV energy production forecasting: a systematic literature review

Abstract

Abstract The growing share of renewable energy, especially solar photovoltaic (PV) and wind, increases the need for reliable power production forecasts beyond the very short term. Medium-term horizons (here defined as forecasts of at least 24 h ahead) are important for planning tasks such as market bidding, reserve sizing, and operational scheduling, but research in this area is diverse and difficult to compare due to differences in horizons, datasets, and evaluation settings. This paper presents a systematic literature review of machine learning approaches for PV and wind production forecasting with horizons $$\ge 24$$ ≥ 24 h. Following PRISMA guidelines, a Scopus search using a broad query (2978 results), followed by a refined query (69 results) and a three-stage screening process (title, abstract, and full text), resulted in 31 included studies. We extract and synthesize evidence on (i) forecast horizons and setups, (ii) model families and hybrid designs, (iii) data sources and the use of measured versus forecast weather inputs, (iv) uncertainty handling (e.g., quantiles, intervals, and scenarios), and (v) links to decision-making contexts. In addition, we propose a taxonomy to classify studies along these dimensions and support comparison across different evaluation settings. The results show that most studies focus on day-ahead and week-ahead evaluation, while 2-week and longer horizons are less common. Hybrid and ensemble approaches are widespread, while transformer-family methods appear in a smaller but visible subset. Most papers rely on measured production data, whereas fewer explicitly use forecast weather (e.g., Numerical Weather Prediction) inputs, even though weather-forecast uncertainty strongly influences multi-day performance in realistic settings. Only a limited number of studies connect forecasts to operational decisions, indicating a gap between accuracy-focused evaluation and decision value. The review highlights the need for more consistent benchmarking, more realistic weather-input evaluation (and, where relevant, other operational exogenous inputs such as curtailment), and more standard uncertainty reporting for medium-term renewable forecasting.