Buildings are one of the major sources of electricity and greenhouse gas emission (GHG) in urban areas all around the world. Since a large integration of solar energy is observed in the form of rooftop photovoltaic (PV) units, electricity use of buildings is highly uncertain due to intermittent nature of solar output power. This leads to poor energy management for both network operators and building owners. In addition, uncertain metrological conditions, diversity and complexity of buildings are big hurdles to accurate prediction of the demand. To improve accuracy of load demand forecast of PV integrated smart building, a hybrid ensemble framework is proposed in this paper. This is based on a combination of five different predictors named as backpropagation neural network (BPNN), Elman neural network (EN), Autoregressive Integrated Moving Average (ARIMA), feed forward neural network (FNN), radial basis function (RBF) and their wavelet transform (WT) models. WT is applied to historical load data to remove the spikes and fluctuations. FNN and RBF network were trained with particle swarm optimization (PSO) for higher forecast accuracy. The output of each predictor in the ensemble network is combined using Bayesian model averaging (BMA). The proposed framework is tested using real data of two practical PV integrated smart buildings in a big university environment. The results indicate that the proposed framework show improvement in average forecast normalized root mean square error (nRMSE) around 17% and 20% in seasonal daily and seasonal weekly case studies, respectively. In addition, proposed framework also produces lowest of nRMSE about 3.88% in seasonal monthly forecast of smart buildings with rooftop PV as compared to benchmark model. The proposed forecast framework provides consistent forecast results for global change institute (GCI) and advance engineering building (AEB) during seasonal daily and weekly comparison.

建筑物是全世界城市地区电力和温室气体排放（GHG）的主要来源之一。由于以屋顶光伏（PV）单元的形式观察到了太阳能的大量整合，由于太阳能输出功率的间歇性，建筑物的用电高度不确定。这导致网络运营商和建筑物所有者的能源管理不佳。此外，不确定的计量条件，建筑物的多样性和复杂性是准确预测需求的主要障碍。为了提高光伏集成智能建筑负荷需求预测的准确性，提出了一种混合集成框架。这是基于五个不同的预测变量的组合，分别称为反向传播神经网络（BPNN），艾尔曼神经网络（EN），自回归综合移动平均值（ARIMA），前馈神经网络（FNN），径向基函数（RBF）及其小波变换（WT）模型。WT适用于历史负荷数据，以消除峰值和波动。对FNN和RBF网络进行了粒子群优化（PSO）训练，以提高预测准确性。使用贝叶斯模型平均（BMA）对集合网络中每个预测变量的输出进行组合。在大型大学环境中，使用两个实际的光伏集成智能建筑的真实数据对所提出的框架进行了测试。结果表明，所提出的框架显示，在季节性每日和季节性每周案例研究中，平均预测归一化均方根误差（nRMSE）分别提高了约17％和20％。此外，提出的框架还产生最低的nRMSE约3。与基准模型相比，屋顶光伏智能建筑的季节性月度预测为88％。拟议的预测框架在每日和每周的比较期间为全球变更研究所（GCI）和高级工程大楼（AEB）提供一致的预测结果。