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•An interpretable data-driven building energy load prediction method is proposed.•A clustering decision trees model is developed to identify operation conditions.•An adaptive multiple linear regression model is proposed for load prediction.•Training multiple models improves accuracy of intrinsically interpretable models.•The proposed method demonstrates high accuracy and good intrinsic interpretability. 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. [Display omitted]

Meanwhile, stocks closed mixed yesterday led by gains in tech and industrial companies. The Dow did decline twenty-six points. The NASDAQ closed up eighteen, hitting a new record. The S&P 500 gained three points.

With the war in Iraq continuing at an enormous financial cost to the United States, the residential housing market taking a significant downturn, the sub-prime mortgage industry in turmoil, automotive industry worker strikes, and oil prices at an all-time high (hovering around $99 per barrel at the time of publication), the economy has certainly taken its knocks, and as Bates predicted, the commercial furniture industry dampened.