Dynamically Optimizing Parameters in Support Vector Regression: An Application of Electricity Load Forecasting

2006 ◽  
Author(s):  
Chin-Chia Hsu ◽  
Chih-Hung Wu ◽  
Shih-Chien Chen ◽  
Kang-Lin Peng
Keyword(s):  
Support Vector Regression ◽  
Load Forecasting ◽  
Support Vector ◽  
Electricity Load ◽  
Electricity Load Forecasting

Comparative Analysis of Statistical, Machine Learning, and Grey Methods for Short-Term Electricity Load Forecasting

2016 ◽  
pp. 1161-1183 ◽  
Author(s):  
Tuncay Ozcan ◽  
Tarik Küçükdeniz ◽  
Funda Hatice Sezgin
Keyword(s):  
Support Vector Regression ◽  
Electricity Market ◽  
Load Forecasting ◽  
Forecast Accuracy ◽  
Grey Model ◽  
Support Vector ◽  
Forecasting Model ◽  
Data Sets ◽  
Electricity Load ◽  
Electricity Load Forecasting

Electricity load forecasting is crucial for electricity generation companies, distributors and other electricity market participants. In this study, several forecasting techniques are applied to time series modeling and forecasting of the hourly loads. Seasonal grey model, support vector regression, random forests, seasonal ARIMA and linear regression are benchmarked on seven data sets. A rolling forecasting model is developed and 24 hours of the next day is predicted for the last 14 days of each data set. This day-ahead forecasting model is especially important in day-ahead market activities and plant scheduling operations. Experimental results indicate that support vector regression and seasonal grey model outperforms other approaches in terms of forecast accuracy for day-ahead load forecasting.

Comparative Analysis of Statistical, Machine Learning, and Grey Methods for Short-Term Electricity Load Forecasting

2016 ◽  
pp. 96-118
Author(s):  
Tuncay Ozcan ◽  
Tarik Küçükdeniz ◽  
Funda Hatice Sezgin
Keyword(s):  
Support Vector Regression ◽  
Electricity Market ◽  
Load Forecasting ◽  
Forecast Accuracy ◽  
Grey Model ◽  
Support Vector ◽  
Forecasting Model ◽  
Data Sets ◽  
Electricity Load ◽  
Electricity Load Forecasting

Electricity load forecasting is crucial for electricity generation companies, distributors and other electricity market participants. In this study, several forecasting techniques are applied to time series modeling and forecasting of the hourly loads. Seasonal grey model, support vector regression, random forests, seasonal ARIMA and linear regression are benchmarked on seven data sets. A rolling forecasting model is developed and 24 hours of the next day is predicted for the last 14 days of each data set. This day-ahead forecasting model is especially important in day-ahead market activities and plant scheduling operations. Experimental results indicate that support vector regression and seasonal grey model outperforms other approaches in terms of forecast accuracy for day-ahead load forecasting.

scholarly journals Electricity Load Forecasting Using Support Vector Regression with Memetic Algorithms

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Zhongyi Hu ◽  
Yukun Bao ◽  
Tao Xiong
Keyword(s):  
Support Vector Regression ◽  
Electricity Markets ◽  
Load Forecasting ◽  
Solution Space ◽  
Memetic Algorithms ◽  
Pattern Search ◽  
Support Vector ◽  
Forecasting Models ◽  
Electricity Load ◽  
Electricity Load Forecasting

Electricity load forecasting is an important issue that is widely explored and examined in power systems operation literature and commercial transactions in electricity markets literature as well. Among the existing forecasting models, support vector regression (SVR) has gained much attention. Considering the performance of SVR highly depends on its parameters; this study proposed a firefly algorithm (FA) based memetic algorithm (FA-MA) to appropriately determine the parameters of SVR forecasting model. In the proposed FA-MA algorithm, the FA algorithm is applied to explore the solution space, and the pattern search is used to conduct individual learning and thus enhance the exploitation of FA. Experimental results confirm that the proposed FA-MA based SVR model can not only yield more accurate forecasting results than the other four evolutionary algorithms based SVR models and three well-known forecasting models but also outperform the hybrid algorithms in the related existing literature.

Electricity Load Forecasting Using Machine Learning Techniques

2010 ◽  
pp. 318-336
Author(s):  
Manuel Martín-Merino Acera
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Load Forecasting ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Strong Impact ◽  
Local Character ◽  
Nonlinear Character ◽  
Electricity Load ◽  
Electricity Load Forecasting

Electricity load forecasting has become increasingly important due to the strong impact on the operational efficiency of the power system. However, the accurate load prediction remains a challenging task due to several issues such as the nonlinear character of the time series or the seasonal patterns it exhibits. A large variety of techniques have been proposed to this aim, such as statistical models, fuzzy systems or artificial neural networks. The Support Vector Machines (SVM) have been widely applied to the electricity load forecasting with remarkable results. In this chapter, the authors study the performance of the classical SVM in the problem of electricity load forecasting. Next, an algorithm is developed that takes advantage of the local character of the time series. The method proposed first splits the time series into homogeneous regions using the Self Organizing Maps (SOM) and next trains a Support Vector Machine (SVM) locally in each region. The methods presented have been applied to the prediction of the maximum daily electricity demand. The properties of the time series are analyzed in depth. All the models are compared rigorously through several objective functions. The experimental results show that the local model proposed outperforms several statistical and machine learning forecasting techniques.

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