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This paper discusses three aspects of demand response through a case study of a US utility: 1) how load profile changes due to demand response; 2) how much energy has been saved annually; 3) whether the load forecasts have been affected by demand response. A regression based approach is deployed to try to answer these questions. The results show that for this particular utility, the demand response programs on average help shave the peaks but do not significantly affect the annual sales. In addition, the forecasting accuracy can be improved by excluding the hours affected by demand response.