A cross-sectional survey of deterministic PV power forecasting: Progress and limitations in current approaches

• Published in: Renewable energy 2024-05, Vol.226, p.120385, Article 120385
• Main Authors: Sabadus, Andreea, Blaga, Robert, Hategan, Sergiu-Mihai, Calinoiu, Delia, Paulescu, Eugenia, Mares, Oana, Boata, Remus, Stefu, Nicoleta, Paulescu, Marius, Badescu, Viorel
• Format: Article
• Language: English
• Subjects: Deterministic forecasting; Forecast scenario; Literature mapping; Photovoltaic power; Quantitative analysis
• ISSN: 0960-1481; 1879-0682
• DOI: 10.1016/j.renene.2024.120385

This review reports a quantitative analysis across the deterministic photovoltaic (PV) power forecasting approaches. Model accuracy tests from papers passing a set of selection criteria are collected in a database, along with the meta-information necessary to describe the forecast scenarios. The selection criteria ensure a framework for inter-comparison analyses. 66 papers were found that pass the selection stage, constituting an arbitrary sample of the literature in terms of forecast scenario. Therefore, this review generates a reliable picture of the state-of-the-art in the accuracy of deterministic PV power forecasting. Despite the apparent wealth of the forecasting studies, a detailed analysis is foreclosed by the small number of entries into the database for different scenarios. Simple and hybrid Machine Learning models are the most popular choice. Studies performed in Europe, Asia and North America are dominant, with the majority of locations having a temperate climate (46/66). The number of studies in arid (10/66) and tropical (8/66) climates is small, even with the high speed development of the PV sector. Physics-based models are weakly represented. A set of recommendations on reporting the results of deterministic forecasts and metadata is proposed. A detailed description of forecast scenarios, employed accuracy metrics, and all operations applied on the data is crucial for ensuring a realistic inter-comparability of models’ performance. •A quantitative analysis across PV power forecasting scenarios is performed.•nRMSE, MAPE and skill score are collected from 66 studies.•Model class, climate and forecast timescales are the most influential factors.•Test set length, metric normalization and other parameters impede inter-comparability.•Numerous gaps and biases found, despite apparent wealth of publications.