Power load forecasting using data mining and knowledge discovery technology. (1st January 2011)

• Record Type: Journal Article
• Authors: Wang, Yongli; Niu, Dongxiao; Ji, Ling
• Is Part Of: International journal of intelligent information and database systems. Volume 5:Number 5(2011)
• Page Start: 452
• Page End: 467
• Publication Date: 2011-01-01
• Subjects: knowledge discovery -- data mining -- DM -- power system -- BP neural network -- power load forecasting -- China; Database management -- Computer programs -- Periodicals; Information retrieval -- Computer programs -- Periodicals; Information storage and retrieval systems -- Computer programs -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligent agents (Computer software) -- Periodicals; 006.33
• Languages: English
• ISSNs: 1751-5858

Abstract

Considering the importance of the peak load to the dispatching and management of the electric system, the error of peak load is proposed in this paper as criteria to evaluate the effect of the forecasting model. This paper proposes a systemic framework that attempts to use data mining and knowledge discovery (DMKD) to pretreat the data. And a new model is proposed which combines artificial neural networks with data mining and knowledge discovery for electric load forecasting. With DMKD technology, the system not only could mine the historical daily loading which had the same meteorological category as the forecasting day to compose data sequence with highly similar meteorological features, but also could eliminate the redundant influential factors. Then an artificial neural network is constructed to predict according to its characteristics. Using this new model, it could eliminate the redundant information, accelerate the training speed of neural network and improve the stability of the convergence. Compared with single BP neural network, this new method can achieve greater forecasting accuracy.