The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Self-Organizing Maps
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Accurate Electricity Load Forecasting with Artificial Neural Networks
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-1 (CIMCA-IAWTIC'06) - Volume 01
Dynamic support vector machines for non-stationary time series forecasting
Intelligent Data Analysis
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Peak load forecasting using the self-organizing map
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Training multilayer perceptron classifiers based on a modified support vector method
IEEE Transactions on Neural Networks
Self organization of a massive document collection
IEEE Transactions on Neural Networks
Bankruptcy analysis with self-organizing maps in learning metrics
IEEE Transactions on Neural Networks
Unsupervised speaker recognition based on competition between self-organizing maps
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Self-organizing nets for optimization
IEEE Transactions on Neural Networks
Identification and control of dynamical systems using the self-organizing map
IEEE Transactions on Neural Networks
Hidden space support vector machines
IEEE Transactions on Neural Networks
New adaptive color quantization method based on self-organizing maps
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Color clustering and learning for image segmentation based on neural networks
IEEE Transactions on Neural Networks
Generalizing self-organizing map for categorical data
IEEE Transactions on Neural Networks
Implementing support vector regression with differential evolution to forecast motherboard shipments
Expert Systems with Applications: An International Journal
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Forecasting of future electricity demand is very important for decision making in power system operation and planning. In recent years, due to privatization and deregulation of the power industry, accurate electricity forecasting has become an important research area for efficient electricity production. This paper presents a time series approach for mid-term load forecasting (MTLF) in order to predict the daily peak load for the next month. The proposed method employs a computational intelligence scheme based on the self-organizing map (SOM) and support vector machine (SVM). According to the similarity degree of the time series load data, SOM is used as a clustering tool to cluster the training data into two subsets, using the Kohonen rule. As a novel machine learning technique, the support vector regression (SVR) is used to fit the testing data based on the clustered subsets, for predicting the daily peak load. Our proposed SOM-SVR load forecasting model is evaluated in MATLAB on the electricity load dataset provided by the Eastern Slovakian Electricity Corporation, which was used in the 2001 European Network on Intelligent Technologies (EUNITE) load forecasting competition. Power load data obtained from (i) Tenaga Nasional Berhad (TNB) for peninsular Malaysia and (ii) PJM for the eastern interconnection grid of the United States of America is used to benchmark the performance of our proposed model. Experimental results obtained indicate that our proposed SOM-SVR technique gives significantly good prediction accuracy for MTLF compared to previously researched findings using the EUNITE, Malaysian and PJM electricity load datasets.