NDLI: An improved new approach for electric capacity forecasting based on historical data of GDP

Content Provider	IEEE Xplore Digital Library
Author	Wen, J.N. Wang, J.Z. Lu, H.Y.
Copyright Year	2008
Description	Author affiliation: Sch. of Math. & Stat., Lanzhou Univ., Lanzhou (Wen, J.N.; Wang, J.Z.)
Abstract	Prediction is important for the electricity capacity management. Accurate prediction can help the policymaker make correct decision and promote the decision making quality. For improving an accuracy of prediction, in this paper, we adopt the theory of grey prediction to develop a new forecasting approach that integrates historical data of the gross domestic products (GDP) into an electric capacity forecasting. We adopted grey prediction as a forecasting means because of its fast calculation with as few as four data inputs needed. As a result, our study considered that Wu and Chen proposed a modeling method of the improved grey relational analysis and main shows that the general grey model, GM (1,1), which is an especial case, is adequate to handle an electrical power system. In this study, the prediction is improved significantly by applying the transformed Grey model and the concept of average system slope. The adaptive value of a in the Grey differential equation is obtained quickly with the average system slope technique. In such a way, the wastage of electric consumption can be avoided. That is, it is another achievement of virtual electric power plant.
Starting Page	2487
Ending Page	2491
File Size	563503
Page Count	5
File Format	PDF
ISBN	9781424417179
DOI	10.1109/ICIEA.2008.4582965
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-03
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	gross domestic products (GDP) Electric capacity forecasting grey prediction grey relational analysis
Content Type	Text
Resource Type	Article

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