Energy Consumption Building

This research proposes a method for estimating the Overall Thermal Transfer Value (OTTV) and the whole building energy consumption of condominium buildings under the Energy Conservation Act of Thailand B.E.2550 by creating a reference
condominium building that complies with the typical design of condominiums and occupancy to determine the base level energy use of a condominium.

The VisualDOE4.0 simulation tool was used in this research, which started with acquiring two types of data. One is a set of condominium shapes in Bangkok and nearby provinces. Another is a set of occupancy profiles. The data were gathered and analyzed in the VisualDOE4.0 program and the parameterization method was used to create the coefficients in the OTTV and the whole building energy (Ec) equations.

The resulting equations suggest that the base level energy use be 126.22 kWh/m2-year and the OTTV of the reference condominium in this research, called “OTTVcondo”,be 26.5 W/m2. The average coefficients of the “OTTVcondo” that comprise TDeq, ΔT and ESR are 5.43, 0.97 and 91.40 respectively. However, energy use results from the proposed equation and computer simulation show discrepancies of -5.9% to 7.2%, which result from the average TDeq, ΔT and ESR applied in the study. It is recommended that a further, detailedstudy regarding the effect of building orientation be carried out.

This paper presents the building heating demand prediction model with occupancy profile and operational heating power level characteristics in short time horizon (a couple of days) using artificial neural network. In addition, novel pseudo dynamic transitional model is introduced, which consider time dependent attributes of operational power level characteristics and its effect in the overall model performance is outlined. Pseudo dynamic model is applied to a case study of French Institution building and compared its results with static and other pseudo dynamic neural network models. The results show the coefficients of correlation in static and pseudo dynamic neural network model of 0.82 and 0.89 (with energy consumption error of 0.02%) during the learning phase, and 0.61 and 0.85 during the prediction phase, respectively. Further, orthogonal array design is applied to the pseudo dynamic model to check the schedule of occupancy profile and operational heating power level characteristics. The results show the new schedule and provide the robust design for pseudo dynamic model. Due to prediction in short time horizon, it finds application for Energy Services Company (ESCOs) to manage the heating load for dynamic control of heat production system.