Abstract
Photovoltaic (PV) power forecasting is important for renewable-energy planning and decision support. Besides accuracy, interpretability is relevant because it helps explain how meteorological variables influence the forecasted PV output. This paper presents an interpretable neuro-fuzzy forecasting model based on feature selection, feature weighting, shared fuzzy sets, and population-based optimization. Input variables are selected using three complementary methods: correlation-based ranking, Random Forest importance, and an ANOVA-based univariate test. The resulting weights are incorporated into a Takagi-Sugeno-Kang Zero-Order system. The results show that the proposed method should be interpreted as an accuracy-interpretability trade-off: selected XGBoost baselines achieve slightly lower MSE values, while the neuro-fuzzy model provides competitive accuracy with explicit fuzzy rules and readable fuzzy sets.
Paper Type
Poster
DOI
10.62036/ISD.2026.44
An Interpretable Neuro-Fuzzy System with Feature Selection for Photovoltaic Power Forecasting
Photovoltaic (PV) power forecasting is important for renewable-energy planning and decision support. Besides accuracy, interpretability is relevant because it helps explain how meteorological variables influence the forecasted PV output. This paper presents an interpretable neuro-fuzzy forecasting model based on feature selection, feature weighting, shared fuzzy sets, and population-based optimization. Input variables are selected using three complementary methods: correlation-based ranking, Random Forest importance, and an ANOVA-based univariate test. The resulting weights are incorporated into a Takagi-Sugeno-Kang Zero-Order system. The results show that the proposed method should be interpreted as an accuracy-interpretability trade-off: selected XGBoost baselines achieve slightly lower MSE values, while the neuro-fuzzy model provides competitive accuracy with explicit fuzzy rules and readable fuzzy sets.
Recommended Citation
Zalasiński, M., Łapa, K. & Szczepanik, T.(2026). An Interpretable Neuro-Fuzzy System with Feature Selection for Photovoltaic Power Forecasting. In M. Valenta, B. Mannová, R. Pergl, A. Przybylek, M. Lang, H. Linger, C. Schneider, N. Iivari, & E. Insfran (Eds.), Making ISD Sustainable: Reloaded with AI and Automation (ISD2026 Proceedings). Prague, Czech Republic: Czech Technical University in Prague. ISBN: 978-80-01-07585-2. https://doi.org/10.62036/ISD.2026.44