A hybrid model for integrated day-ahead electricity price and load forecasting in smart grid

Load and price forecasting are two key issues for market participants and system operators in electricity markets. Most existing works predict load and price separately. However, a dynamic pricing type scenario is envisioned in smart grid, where consumers may have the ability to react to constantly changing price signals by shifting electricity usages from expensive hours to others, which would consequently impact electricity prices. Thus, price and load signals are strongly coupled, and previous separated forecast models may not be as effective. In this study, a two-stage integrated price and load forecasting framework is developed. The first stage provides initial price and load forecasts separately, and the second stage considers load and price interaction with initial forecasts as inputs. At each stage, a hybrid time-series and adaptive wavelet neural network (AWNN) model is used, in which multivariate autoregressive integrated moving average catches the linear relationship of price and load log return series, generalised autoregressive conditional heteroscedastic unveils heteroscedastic character of residuals and AWNN presents non-linear impacts. Several criteria such as average mean absolute percentage error and error variance are used to measure the forecasting accuracy. Illustrative forecasting examples of the New York Independent System Operator market are presented to show the efficiency of the proposed method.