Buildings are one of the significant sources of energy consumption and greenhouse gas emission in urban areas all over the world. Lighting control and building integrated photovoltaic (BIPV) are two effective measures in reducing overall primary energy consumption and carbon emission during building operation. Due to the complex energy nature of the building, accurate day-ahead prediction of heating, cooling, lighting loads and BIPV electrical power production is essential in building energy management. Owing to the changing metrological conditions, diversity and complexity of buildings, building energy load demands and BIPV electrical power production is highly variable. This may lead to poor building energy management, extra primary energy consumption or thermal discomfort. In this study, three machine learning-based multi-objective prediction frameworks are proposed for simultaneous prediction of multiple energy loads. The three machine learning techniques are artificial neural network, support vector regression and long-short-term-memory neural network. Since heating, cooling, lighting loads and BIPV electrical power production share similar affecting factors, it is computational time saving to adopt the proposed multi-objective prediction framework to predict multiple building energy loads and BIPV power production. The ANN-based predictive model results in the smallest mean absolute percentage error while SVM-based one cost the shortest computation time.

建筑物是全世界城市地区能源消耗和温室气体排放的重要来源之一。照明控制和建筑物集成光伏（BIPV）是减少建筑物运行期间总体一次能源消耗和碳排放的两个有效措施。由于建筑物的能源性质复杂，因此在建筑物能源管理中，准确预测供暖，制冷，照明负荷和BIPV电力生产的日前需求至关重要。由于不断变化的计量条件，建筑物的多样性和复杂性，建筑物的能源负荷需求和BIPV电力生产是高度可变的。这可能导致不良的建筑能源管理，额外的一次能源消耗或热不适。在这个研究中，提出了三种基于机器学习的多目标预测框架，用于同时预测多个能量负荷。三种机器学习技术是人工神经网络，支持向量回归和长短期记忆神经网络。由于供暖，制冷，照明负荷和BIPV电力生产具有相似的影响因素，因此采用建议的多目标预测框架来预测多个建筑物的能源负荷和BIPV电力生产将节省计算时间。基于ANN的预测模型导致最小的平均绝对百分比误差，而基于SVM的模型则花费了最短的计算时间。支持向量回归和长短期记忆神经网络。由于供暖，制冷，照明负荷和BIPV电力生产具有相似的影响因素，因此采用建议的多目标预测框架来预测多个建筑物的能源负荷和BIPV电力生产可以节省计算时间。基于ANN的预测模型导致最小的平均绝对百分比误差，而基于SVM的模型则花费了最短的计算时间。支持向量回归和长短期记忆神经网络。由于供暖，制冷，照明负荷和BIPV电力生产具有相似的影响因素，因此采用建议的多目标预测框架来预测多个建筑物的能源负荷和BIPV电力生产将节省计算时间。基于ANN的预测模型导致最小的平均绝对百分比误差，而基于SVM的模型则花费了最短的计算时间。