Luiz Friedrich and Afshin Afshari
Urban energy modeling, regression, demand-side management, forecasting
Climate change, pollution, reduced infrastructure invest-ment availability and escalating fossil fuel prices have resulted in renewed emphasis on energy conservation and efficient electricity infrastructure utilization through De-mand Side Management (DSM) in the existing building stock. DSM measures ranging from enhanced building controls to equipment/envelope retrofits are designed to address this problem. The difficulty to accurately assess the ex-post impact of such measures is a widely recog-nized barrier to the wider deployment of DSM. The task is complicated by the dynamic nature of the energy con-suming processes, the coupled interaction of multiple sub-systems and the high correlation of demand with weather and other perturbations. An hourly regression-based model of the load, driven by exogenous variables is pro-posed to address this problem. The model was estimated for the city of Abu Dhabi, UAE, using measured data from pre-DSM period. It was then used to profile the “baseline” energy consumption over a selected post-DSM period revealing, though comparison with the actual ener-gy consumption, the savings attributable to the DSM intervention. The model produced accurate results; ad-justed R-squared of 0.9931 (training period - year 2010), a RMSE equivalent to 1.84% of the annual peak load, and a MAPE of 2.64% (verification data-set first-half 2011).
Important Links:
Go Back
