Meta-learning strategy based on user preferences and a machine recommendation system for real-time cooling load and COP forecasting

Abstract Building data forecasting plays an increasingly important role in building energy savings. However, the one-fits-all model cannot satisfy all the requirements of multiple application scenarios and user preferences. Motivated by the need to bridge the research gap between different user preferences (application scenarios) and energy prediction model recommendation systems, this paper proposes a novel meta-learning strategy based on an artificial neural network recommendation system. This strategy is employed for real-time cooling loads, coefficients of performance prediction and optimal prediction model recommendations. The data set is composed of 40 cases from five factory buildings. After the predictions and recommendations are obtained for all cases, the two-stage user preferences are considered based on multi-objective decision-making algorithms. Then, a new model termed the “walking slide method”, is proposed to predict some special cases. This study shows that the seasonal autoregressive integrated moving average model and random forest model achieve the best prediction accuracy and the minimum computation cost separately for most cases, while the long short-term memory is the best model when considering the two criteria. The variances between the different cases lead to a lower cross-validation score (approximately 65%), but a higher success rate (over 99%) for the recommendation performance. In addition, in the more complex application scenarios, a lower prediction accuracy and recommendation success rate will be obtained. In most cases, the use of a prediction combined with a monitoring system is the best choice. Last, the reliability of the results is verified by application studies. This work provides a scientific basis for energy prediction applications based on user preferences.