It is widely accepted that long-run elasticities of demand for electricity are not stable over time. We model long-run sectoral electricity demand using a time-varying cointegrating vector. Specifically, the coefficient on income (residential sector) or output (commercial and industrial sectors) is allowed to follow a smooth semiparametric function of time, providing a flexible specification that allows more accurate out-of-sample forecasts than either fixed or discretely changing regression coefficients.
This paper proposes a novel approach to measure and analyze the effect of temperature on electricity demand. This temperature effect is specified as a function of the density of temperatures observed at a high frequency with a functional coefficient, which we call the temperature response function. This approach contrasts with the usual approach to model the temperature effect as a function of heating and cooling degree days.