Abstract
This research studies short-term electricity load prediction with a large-scalelinear programming support vector regression (LP-SVR) model. The LP-SVR is compared with other three non-linear regression models: Collobert’s SVR, Feed-Forward Neural Networks (FFNN), and Bagged Regression Trees (BRT). The four models are trained to predict hourly day-ahead loads given temperature predictions, holiday information and historical loads. The models are trained on-hourly data from the New England Power Pool (NEPOOL) region from 2004 to 2007 and tested on out-of-sample data from 2008. Experimental results indicate that the proposed LP-SVR method gives the smallest error when compared against the other approaches. The LP-SVR shows a mean absolute percent error of 1.58% while the FFNN approach has a 1.61%. Similarly, the FFNN method shows a 330 MWh (Megawatts-hour) mean absolute error, whereas the LP-SVR approach gives a 238 MWh mean absolute error. This is a significant difference in terms of the extra power that would need to be produced if FFNN was used. The proposed LP-SVR model can be utilized for predicting power loads to a very low error, and it is comparable to FFNN and over-performs other state of the art methods such as: Bagged Regression Trees, and Large-Scale SVRs.
Highlights
Accurate load predictions are critical for short term operations and long term utilities planning
The general problem of electricity load forecasting has been approached with a combination of support vector machines and simulated annealing with satisfactory results [3] when compared against neural network approaches for regression; the problem was not addressed for the particular case of short-term electricity load forecasting
This paper shows that the proposed large-scalelinear programming support vector regression (LP-SVR) model provides better forecasts than Feed Forward Neural Network (FFNN) and Bagged Regression Trees (BRT) approaches
Summary
Accurate load predictions are critical for short term operations and long term utilities planning. The main disadvantage of these is that they offer no transparency into how the predicted load is calculated They ignore important information, e.g., regional loads and weather patterns. The general problem of electricity load forecasting has been approached with a combination of support vector machines and simulated annealing with satisfactory results [3] when compared against neural network approaches for regression; the problem was not addressed for the particular case of short-term electricity load forecasting. To the best of our knowledge, no efforts have been reported to address the problem of short-term electricity load forecasting using a large-scale approach to support vector machines. This research considers several variables to build a prediction model and compares results among a Linear Programming Support Vector Regression (LPSVR) approach, a Feed Forward Neural Network (FFNN), and Bagged Regression Trees (BRT). This paper shows that the proposed LP-SVR model provides better forecasts than FFNN and BRT approaches
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