Customer information systems for deregulated ASEAN countries

Abstract

-In similar fashion to western countries, ASEAN countries are also gearing up towards deregulation. Despite potentially different motivating drivers, the ultimate objectives are free market competition leading to efficient pricing signals as well as providing customers with the freedom to choose their electricity provider and benefit from competitive prices. This paper provides an ASEAN electricity market analysis and describes the development of electricity deregulation in ASEAN countries. By way of background it also highlights the objectives of deregulation, the potential challenges and also the impact areas focusing on existing Customer Information Systems (CIS) that have been developed by other utilities. In addition, this paper proposes a new framework for improving CIS for ASEAN utilities facing deregulation. The framework outlines a CIS, which has intelligent features enabling the utility to estimate and predict customer behaviour with respect to consumption patterns. It describes how these features can assist the utility companies to retain their existing customers as well as attract more customers.

Customer Information System Data Pre-Processing with Feature Selection Techniques for Non-Technical Losses Prediction in an Electricity Market

“…This is a worldwide problem faced not only by most of the least developed countries in Africa and Asia, but also by developed countries such as the United States of America and the United Kingdom [5]. More specifically, high rates of NTLs are reported in the majority of developing countries in the ASEAN group, including Indonesia, Malaysia, and Thailand [4,6]. The percentage of losses reported may differ from country to country depending on various factors, including the degree of political stability, economic and social patterns [5].…”

Power Utility Nontechnical Loss Analysis With Extreme Learning Machine Method

“…In [13] and [14] the authors proposed a method to detect NTLs using the load patterns of customers derived from the customer database. The approach proposed in the present paper extends the research work of [15] to include more advanced data-mining techniques that deliver improved accuracy and efficiency.…”