Clustering Methods for Electricity Consumers: An Empirical Study in Hvaler-Norway

The development of Smart Grid in Norway in specific and Europe/US in general will shortly lead to the availability of massive amount of fine-grained spatio-temporal consumption data from domestic households. This enables the application of data mining techniques for traditional problems in power system. Clustering customers into appropriate groups is extremely useful for operators or retailers to address each group differently through dedicated tariffs or customer-tailored services. Currently, the task is done based on demographic data collected through questionnaire, which is error-prone. In this paper, we used three different clustering techniques (together with their variants) to automatically segment electricity consumers based on their consumption patterns. We also proposed a good way to extract consumption patterns for each consumer. The grouping results were assessed using four common internal validity indexes. We found that the combination of Self Organizing Map (SOM) and k-means algorithms produce the most insightful and useful grouping. We also discovered that grouping quality cannot be measured effectively by automatic indicators, which goes against common suggestions in literature.Comment: 12 pages, 3 figure

Energy Efficient Energy Analytics

Smart meters allow for hourly data collection related to customer's power consumption. However this results in thousands of data points, which hides broader trends in power consumption and makes it difficult for energy suppliers to make decisions regards to a specific customer or to large number of customers. Since data without analysis is useless, various algorithms have been proposed to lower the dimensionality of data, discover trends (eg. regression), study relationships between different types (eg. temperature and power data) of collected data, summarize data (e.g. histogram). This allows for easy consumption by the end user. The smart meter data is very compute intensive to process as there are a large number of houses and each house has the data collected over a few years. To speed up the smart meter data analysis, computer clusters have been used. Ironically, these clusters consume a lot of power. Studies have shown that about 10 % of power is consumed by the computing infrastructure. In this thesis a GPU will be used to perform analysis of smart meter data and it will be compared to a baseline CPU implementation. It will also show that GPUs are not only faster than the CPU, but they are also more power efficient