Lessons Learnt from Mining Meter Data of Residential Consumers

Abstract

IntroductionAdvanced metering infrastructure enables automated load management and execution of demand response which is mainly designed to reduce peak demand, carbon emissions, and cost, providing a great opportunity for electricity market participations, improving voltage efficiency and distribution network automation [1]. However, network operators and suppliers should not only design and implement appropriate DR programs, but they should also be well-equipped to control the power network at all times, anticipate the future events to apply optimal solutions, while bidding in the electricity market to gain the maximum economic benefits.Knowledge discovery in databases (KDD) for analysing the behaviour of different types of end-users in consuming energy have become of great interest in recent energy-related research. The evolution of smart meters is shifting the management of demand from supply-focused to supply & consumers-focused [2] which provides a great opportunity to activate demand response programs.Having more knowledge about different customer's behaviour in how and when they use the electricity can help the network operators and energy suppliers for more correctly action in a liberalized and competitive electricity market in a dynamic form [3].In recent years, various methods of electricity load data characterization have been introduced and applied. The design of such methods depends on the motivation of using that data and the volumes of data available [4][5][6].Moreover, with the information extracted from smart meters, the power network would be able to cluster electricity consumers with monitoring their energy usages and data mining in their load profiles patterns during a determined time period. Such of these segmentations provides various higher-level applications such more accurate load forecasting, theft detection, determining required demand reduction and specific demand response programs etc.The parameters that can affect residential usage patterns can be examined by identifying typical load profiles for each cluster of different dwellings. Comparing the different demand pattern can help utilities to design and apply demand response