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Evaluate and interpret the performance of artificial neural network (ANN) models for building cooling-load prediction using key metrics—MAE, MSE, and R-squared. Summarize tuning strategies for model optimization and the implications of these metrics, noting that model selection can be erratic, MAE reflects consistent small errors, MSE highlights large errors and sensitivity to outliers, while R-squared provides a consistent measure of overall fit. For feature contribution, compare variable importance plots from the OLDEN and GARSON algorithms, especially the dominance of glazing area, and report performance (e.g., MAE = 2.04, MSE = 7.07, R² = 0.92). In addition, contrast ANN cooling-load prediction with statistical models applied to heating-load prediction, emphasizing greater deviations, heightened outlier sensitivity, and reduced accuracy observed in conventional approaches. See more