Abstract
Currently, in most countries, the electricity sector is liberalized, and electricity is traded in deregulated electricity markets. In these markets, electricity demand is determined the day before the physical delivery through (semi-)hourly concurrent auctions. Hence, accurate forecasts are essential for efficient and effective management of power systems. The electricity demand and prices, however, exhibit specific features, including non-constant mean and variance, calendar effects, multiple periodicities, high volatility, jumps, and so on, which complicate the forecasting problem. In this work, we compare different modeling techniques able to capture the specific dynamics of the demand time series. To this end, the electricity demand time series is divided into two major components: deterministic and stochastic. Both components are estimated using different regression and time series methods with parametric and nonparametric estimation techniques. Specifically, we use linear regression-based models (local polynomial regression models based on different types of kernel functions; tri-cubic, Gaussian, and Epanechnikov), spline function-based models (smoothing splines, regression splines), and traditional time series models (autoregressive moving average, nonparametric autoregressive, and vector autoregressive). Within the deterministic part, special attention is paid to the estimation of the yearly cycle as it was previously ignored by many authors. This work considers electricity demand data from the Nordic electricity market for the period covering 1 January 2013–31 December 2016. To assess the one-day-ahead out-of-sample forecasting accuracy, Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) are calculated. The results suggest that the proposed component-wise estimation method is extremely effective at forecasting electricity demand. Further, vector autoregressive modeling combined with spline function-based regression gives superior performance compared with the rest.
Highlights
Liberalization of the energy sector, changes in climate policies, and the upgrade of renewable energy resources have completely changed the structure of the previous strictly-controlled energy sector
Special attention is paid to the estimation of the yearly cycle as it was previously ignored by many authors
The results suggest that the proposed component-wise estimation method is extremely effective at forecasting electricity demand
Summary
Liberalization of the energy sector, changes in climate policies, and the upgrade of renewable energy resources have completely changed the structure of the previous strictly-controlled energy sector. Holt–Winters (D-HW) model, and Multiple Equations Time Series (MET) approaches are used for short-term load forecasting [15,16]. The main idea behind this approach is to consider the daily demand profile as a single functional object; functional approaches can be applied to electricity load series. The main objective of this work is to compare different modeling techniques for electricity demand forecasting. The authors suggest to estimate jointly the effect of the long-term trend and yearly cycle using one component [36,37]. The yearly component shows regular cycles, while the long-term component highlights the trend structure of the data These two components must be modeled separately [26].
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