Smart Grids are making use of information and communications technology (ICT) to improve the reliability and flexibility of traditional power grids. This technology depends more and more on methods like load modeling, state estimation, and load forecasting methods. All of these methods are aiming to benefit the whole network analysis to make it become more accurate. Among these methods, load modeling is a very important part of analysis of the whole power network and can offer a good fundamental to other analysis methods. Due to the highly stochastic nature of electricity consumption with many uncertainties, various statistical and classification techniques based on fast data collection are required to help improving the accuracy of conventional load modeling. Nowadays widely used automatic meter reading (AMR) technology in Finland makes it possible to collect customers' hourly load measurements and to use mature clustering methods to analyze those huge sets of customer data and give a better prediction.
In this thesis, some basic classification and regression concepts are borrowed from statistics or machine learning field to help us to analyze the electric customer behavior between di fferent years. This thesis aims to detect either load level change or load shape change of electric customers. K-means and Fuzzy C-means (FCM) are two main methods implemented in MATLAB environment to analyze the load curves. It successfully detects various obvious load pattern changes on different customer types. For the question that when the customer behavior change happens during a year, this thesis can just offer the time information regarding at which week the change happens rather than the specific date. Because we mainly consider the obvious change that lasts for at least one week and ignore temporary changes. The change detection accuracy may be improved in future by more sophisticated methods
