Heating Load Prediction with Meta-Heuristic Algorithms and Adaptive Neuro-Fuzzy Inference System Integration

The anticipation of heating demands is critical in energy saving as well as emissions mitiga-tion. Accurate forecasting of energy demands, combined with detailed studies on renovation possibilities, is essential in building management and is becoming increasingly important in both research and practical applications. Addressing this need, this study adopts a holistic ap-proach by integrating advanced optimization algorithms with precise heating load prediction techniques. Heating load systems present a complex landscape where energy optimization challenges require in-depth investigation and innovative problem-solving. To enhance predic-tive accuracy, two meta-heuristic algorithms, the Reptile Search Algorithm (RSA) and the Flow Direction Algorithm (FDA), are seamlessly integrated into the Adaptive Neuro-Fuzzy Inference System (ANFIS) model. These algorithms utilize heating load data collected and validated through prior stability tests. The study demonstrates that combining meta-heuristic optimization with ANFIS significantly improves prediction accuracy, offering a promising approach for more efficient energy management strategies. The research introduces three models: ANFD (ANFIS+FDA), ANRS (ANFIS+RSA), and an independent ANFIS model, each providing valuable insights for precise heating load prediction. It is important to consider that ANRS model is a notable performer with a great R2 measure equaling 0.991 in addition to a very small RMSE measure equaling 0.951. Such impressive performances reflect that ANRS model is a great predictor of heating load outcomes.