Abstract
Forecasting hourly spot prices for real-time electricity markets is a key activity in economic and energy trading operations. This paper proposes a novel two-stage approach that uses a combination of Auto-Regressive Integrated Moving Average (ARIMA) with other forecasting models to improve residual errors in predicting the hourly spot prices. In Stage-1, the day-ahead price is forecasted using ARIMA and then the resulting residuals are fed to another forecasting method in Stage-2. This approach was successfully tested using datasets from the Iberian electricity market with duration periods ranging from one-week to ninety days for variables such as price, load and temperature. A comprehensive set of 17 variables were included in the proposed model to predict the day-ahead electricity price. The Mean Absolute Percentage Error (MAPE) results indicate that ARIMA-GLM combination performs better for longer duration periods, while ARIMA-SVM combination performs better for shorter duration periods.
Highlights
Electricity price forecasting is a branch of energy forecasting that focuses on predicting the spot and day-ahead prices in the electricity market
We evaluate the performance of our forecast models through a statistical measure known as Mean Absolute Percentage Error (MAPE) (Mean Average Percentage Error) which represents the daily error in price predictions
The results show that the Auto-Regressive Integrated Moving Average (ARIMA)-Support Vector Machine (SVM) method outperformed other hybrid models for smaller dataset such as one week and two weeks, while for larger dataset ARIMA-Generalized Linear Model (GLM) showed superiority than the other hybrid models
Summary
Electricity price forecasting is a branch of energy forecasting that focuses on predicting the spot and day-ahead prices in the electricity market. Price forecasting is one of the fundamental tasks in utilities and energy trading entities for various decision-making mechanisms, for example, adjusting bids to maximize profits, scheduling outages and establishing load profiles. More accurate short-term price forecasts benefit both producers and consumers, as they can maximize profit and minimize the cost of a variety of applications such as home energy management programs in dynamic pricing environments and demand response. Electricity price is highly unstable in the open market or for consumers and its instability further increases by the deployment of the smart grid as it is influenced by many visible and invisible factors. Short-term price (e.g., hourly scales) depends on current demand, type of energy used for generation, historical price trend, hour of days and so forth. Most of the research on price prediction uses these factors as input features for prediction models
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