Electricity Load Profile Determination by using Fuzzy CMeans and Probability Neural Network

Abstract

This paper shows the determination of typical load profiles (TLPs) by using Fuzzy C-Means (FCM) clustering and Probability Neural Network (PNN) classification method. Load profiles provide useful information on electricity consumption to both consumers and suppliers. Precise knowledge on consumer's electricity consumption becomes essential for the suppliers to design tariffs, load balancing, load planning, etc. As for consumers, such knowledge is important for them to keep track of their electricity consumption and to enable them to take part in retail market especially for those who do not have appropriate metering equipment. Hence, an effective, fast and cheap method to obtain load profile has to be developed. Objectives of this paper are to obtain groups of TLPs by using FCM clustering and to assign TLPs using PNN algorithm. One of the main issues in FCM clustering is the determination of optimal number of cluster. This paper proposes optimal number of cluster determination by using Davies-Bouldin index. PNN network is trained to directly allocate the TLPs to the representative groups of consumers. The data used in this project are obtained from Tenaga Nasional Berhad (TNB).