Adaptive Neuro-Fuzzy Inference System for High-Accuracy Flexible Power Point Prediction in Utility-Scale Grid-Connected Photovoltaic Plants

Abstract

Grid-connected photovoltaic (PV) systems integrated into industrial and institutional buildings are critical components of sustainable built environments, where accurate real-time power estimation underpins smart energy management, demand–supply balancing, and reduced dependence on the utility grid. This study develops and validates an Adaptive Neuro-Fuzzy Inference System (ANFIS) for predicting of the flexible power point (FPP) in a 117.76 kWp rooftop PV plant serving a technical workshop facility in northwestern Algeria. The proposed model uses environmental inputs (solar irradiance, ambient temperature, module temperature) and electrical inputs (load power, grid power) acquired from a supervisory monitoring infrastructure to predict the PV system’s FPP under real operating conditions in the built environment. A dataset of 24,479 valid samples spanning 85 distinct calendar days (1 May to 24 July 2025) was collected and preprocessed through cleaning, filtering, and feature-specific normalization. To ensure rigorous out-of-sample evaluation, three complementary validation strategies were implemented: (S1) a random day-based split (60 train/11 test days), (S2) a strictly chronological 70/15/15% split (50/11/10 days), and (S3) an external 14-day hold-out (11–24 July 2025) excised before any training, tuning or model selection step. Statistical analysis reveals strong nonlinear dependence of PV power on solar irradiance and module temperature, with correlations r≈0.93 between irradiance and module temperature, r≈0.82 between irradiance and PV power, and r≈0.95 between load and grid power, highlighting the importance of accurate predicting for facility-level energy management. The ANFIS model achieves R2=0.9992, RMSE =653.62 W and MAE =276.90 W on the random-split test set; R2=0.9998, RMSE =325.40 W and MAE =119.17 W on the chronological test set and R2=0.9997–0.9998, RMSE =363.45–408.50 W on the external 14-day hold-out that was never seen during training. Comparative experiments with k-Nearest Neighbors, Decision Tree, Random Forest, Support Vector Machine, and a Deep Neural Network show that ANFIS is the only model maintaining sub-700 W RMSE on every split, whereas all five benchmarks degrade sharply under chronological and external evaluation (e.g., SVM 2225 → 5198 W; Decision Tree 7440 → 8058 W; DNN 1576 → 2576 W). The persistence of test/external RMSE below the training RMSE on data never used during model construction empirically rules out data leakage as a cause of the high accuracy. These results demonstrate that the proposed, interpretable neuro-fuzzy framework offers a robust and accurate tool for PV power estimation in building-integrated systems, supporting smart energy management and improved performance of energy-intensive built environments.