Combined use of unsupervised and supervised learning for daily peak load forecasting

Abstract In this paper, we have aimed to present a hybrid neural network model for daily electrical peak load forecasting (PLF). Since peak loads usually follow similar patterns, classification of data improves the accuracy of the forecasts. Several factors in peak load, e.g. weather temperature, relative humidity, wind speed and cloud cover, were introduced into the model in order to enhance forecast quality. Most classification attempts in the literature have been intuitive and empty of justification. In this paper, we have proposed a novel approach for clustering data by using a self-organizing map. The Davies–Bouldin validity index was introduced to determine the best clusters. A feed forward neural network (FFNN) has been developed for each cluster to provide the PLF. Eight training algorithms have also been used in order to train the proposed FFNNs. Applying principal component analysis (PCA) decreased the dimensions of the network’s inputs and led to simpler architecture. To evaluate the effectiveness of the proposed hybrid model (PHM), forecasting has been performed by developing a FFNN that uses the un-clustered data. The results proved the superiority and effectiveness of the PHM. Linear regression (LR) models have also been developed for PLF, and the results indicated that the PHM produces considerably better forecasts than those of LR models. Furthermore, the results show that the suggested clustering approach significantly improves the forecasting results on regression analysis too.

[1]  Otávio Augusto S. Carpinteiro,et al.  A hierarchical neural model in short-term load forecasting , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[2]  T. Kohonen,et al.  Bibliography of Self-Organizing Map SOM) Papers: 1998-2001 Addendum , 2003 .

[3]  Otávio Augusto S. Carpinteiro,et al.  A hierarchical neural model in short-term load forecasting , 2004, Appl. Soft Comput..

[4]  In-Keun Yu,et al.  Kohonen neural network and wavelet transform based approach to short-term load forecasting , 2002 .

[5]  Mohammad Reza Amin-Naseri,et al.  A hybrid neural network model for daily peak load forecasting using a novel clustering approach , 2006, Artificial Intelligence and Soft Computing.

[6]  M. K. Soni,et al.  Artificial Neural Network-Based Peak Load Forecasting Using Conjugate Gradient Methods , 2002, IEEE Power Engineering Review.

[7]  H. Mori,et al.  Deterministic Annealing Clustering for ANN-Based Short-Term Load Forecasting , 2001, IEEE Power Engineering Review.

[8]  Alireza Khotanzad,et al.  ANNSTLF-Artificial Neural Network Short-Term Load Forecaster- generation three , 1998 .

[9]  M. K. Soni,et al.  Artificial neural network based peak load forecasting using Levenberg-Marquardt and quasi-Newton methods , 2002 .

[10]  Athanasios Kehagias,et al.  A Bayesian Multiple Models Combination Method for Time Series Prediction , 2001, J. Intell. Robotic Syst..

[11]  Chia-Yon Chen,et al.  Regional load forecasting in Taiwanapplications of artificial neural networks , 2003 .

[12]  Kostas S. Metaxiotis,et al.  Artificial intelligence in short term electric load forecasting: a state-of-the-art survey for the researcher , 2003 .

[13]  Y. Fukuyama,et al.  A novel daily peak load forecasting method using analyzable structured neural network , 2002, IEEE/PES Transmission and Distribution Conference and Exhibition.

[14]  Rastko Zivanovic.  Local Regression-Based Short-Term Load Forecasting , 2001, J. Intell. Robotic Syst..

[15]  Otávio Augusto S. Carpinteiro,et al.  A Hierarchical Self-Organizing Map Model in Short-Term Load Forecasting , 2001, J. Intell. Robotic Syst..

[16]  Nima Amjady,et al.  Short-term hourly load forecasting using time-series modeling with peak load estimation capability , 2001 .

[17]  Rey-Chue Hwang,et al.  A new artificial intelligent peak power load forecaster based on non-fixed neural networks , 2002 .

[18]  Spyros G. Tzafestas,et al.  Computational Intelligence Techniques for Short-Term Electric Load Forecasting , 2001, J. Intell. Robotic Syst..

[19]  Carlos E. Pedreira,et al.  Neural networks for short-term load forecasting: a review and evaluation , 2001 .

[20]  Esa Alhoniemi,et al.  Clustering of the self-organizing map , 2000, IEEE Trans. Neural Networks Learn. Syst..

[21]  Martin T. Hagan,et al.  Neural network design , 1995 .