Buildings are one of the major sources of greenhouse gas emissions and electricity consumption in urban areas all around the world. The load demand of large buildings is highly uncertain due to large penetration of solar PV. As a result, it leads to serious power system stability and quality issues for network operators and energy managers. Therefore, accurately forecast the load demand of buildings is utmost important to design a better energy management system and reduce greenhouse gas emission. However, variable PV output power, random nature of weather, diversity and complexity of buildings are big hurdles to accurately predict the load demand. In this paper, a deterministic hybrid intelligent forecast framework is proposed based on a combination of cascade-forward back-propagation network (CFBPN) in ensemble network for accurate load demand forecast. The wavelet transform (WT) technique is applied to handle the sharp spikes and fluctuations in historical load demand data. In addition, particle swarm optimization (PSO) is used to train the CFBPN in ensemble network for better forecast accuracy. In proposed forecast framework, historical load demand data, temperature, wind speed and humidity are applied as ensemble model inputs. The performance of proposed model is analyzed for one day and 12 hours ahead load demand forecast of summer (S), autumn (A), winter (W) and spring (SP) days. The proposed forecast model provides higher forecast accuracy compared to autoregressive (AR) and wavelet transformed backpropagation neural network (WT+BPNN).