Black-box models have demonstrated remarkable accuracy in forecasting building energy loads. However

they usually lack interpretability and do not incorporate domain knowledge

making it difficult for users to trust their predictions in practical applications. One important and interesting question remains unanswered: is it possible to use intrinsically interpretable models to achieve accuracy comparable to that of black-box models? With an aim of answering this question

this study proposes an intrinsically interpretable machine learning-based method to forecast building energy loads. It creatively combines two intrinsically interpretable machine learning algorithms: clustering decision trees and adaptive multiple linear regression. Clustering decision trees aim to automatically identify various building operation conditions

allowing for the training of multiple models tailored to each condition. It can reduce the complexity of model training data

leading to higher accuracy. Adaptive multiple linear regression is an improved regression algorithm tailored to building energy load prediction. It can adaptively modify regression coefficients according to building operations

enhancing the non-linear fitting capability of multiple linear regression. The proposed method is evaluated utilizing the operational data from an office building. The results indicate that the proposed method exhibits comparable accuracy to both random forests and extreme gradient boosting. Furthermore

it shows significantly superior accuracy

with an average improvement of 10.2 %

compared with some popular black-box algorithms such as artificial neural networks

support vector regression

and classification and regression trees. As for model interpretability

the proposed method reveals that historical cooling loads are the most crucial for predicting building cooling loads under most conditions. Additionally

outdoor air temperature has a significant contribution to building cooling load prediction during the daytime on weekdays in summer and transition seasons. In the future

it will be valuable to explore integrating the laws of physics into the proposed method to further enhance its interpretability.