When typical meteorological year (TMY) data are used as an input to simulate the energy used in a building, it is not clear which hours in the weather data file might correspond to an electric or natural gas utility’s peak demand. Yet, the determination of peak demand impacts is important in utility resource planning exercises and in determining the value of demand-side management (DSM) actions. We propose a formal probability-based method to estimate the summer and winter peak demand reduction from an energy efficiency measure when TMY data and model simulations are used to estimate peak impacts. In the estimation of winter peak demand impacts from some example energy efficiency measures in Texas, our proposed method performs far better than two alternatives. In the estimation of summer peak demand impacts, our proposed method provides very reasonable results which are very similar to those obtained from the Heat Wave approach adopted in California.
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