On the parametric and nonparametric prediction methods for electricity load forecasting

Erişen, Esra
Accurate electricity load forecasting is of great importance in deregulated electricity markets. Market participants can reap significant financial benefits by improving their electricity load forecasts. Electricity load exhibits a complex time series structure with nonlinear relationships between the variables. Hence, new models with higher capabilities to capture such nonlinear relationships need to be developed and tested. In this thesis, we present a parametric and a nonparametric method for short-term and long-term load forecasting, and we compare the performance of these models for different lead times ranging from one hour to one week. These methods include a modified version of Holt Winters Double Seasonal Exponential Smoothing (m-HWT) we present and a Nonlinear Autoregressive with Exogenous Inputs (NARX) neural network model. Using hourly load data from the Dutch electricity grid, an extensive empirical study is carried out for five different provinces. In the second part of the study, NARX is applied to long-term load forecasting in one Dutch province. Our results indicate that NARX clearly outperforms m-HWT in one-hour ahead forecasting. Additionally, our modification to HWT leads to significant improvement in model accuracy especially on special days. Despite its simplicity, m-HWT outperformed NARX for 6 and 12-hours ahead forecasts in general. However NARX performs better in 24-hours, 48-hours and 1 week ahead forecasting. In addition, NARX performs superior to m-HWT in terms of maximum error and on short special days. Computational results also indicate that with a well-trained closed loop NARX neural network model, electricity load can be forecasted successfully one and a half years ahead for hourly intervals. NARX can successfully capture nonlinear effects of special days and temperature. NARX has brought a performance improvement of 30% in terms of mean absolute percent error (MAPE) compared to the existing methodology; time shifting.


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Citation Formats
E. Erişen, “On the parametric and nonparametric prediction methods for electricity load forecasting,” M.S. - Master of Science, Middle East Technical University, 2013.