DATA MINING OF PUBLIC SECTOR INFORMATION FOR ELECTRICITY DISTRIBUTION NETWORK PLANNING AND FORECASTING

The modern society is increasingly dependent of uninterrupted and affordable electricity distribution. For distribution system operator, good planning and decision making are fundamental in order to meet the high standards set by the customers, shareholders and regulator. In recent years, the large amount of data from smart meters and other external sources have opened new possibilities to improve planning by deriving knowledge using advanced machine learning techniques, including data mining and artificial intelligence. In particular, public sector information holds great potential for distribution system operators. It offers readily available and often the only source of information on the structure and development of the environment and society. However, the quality of public sector information is in some cases affected by issues such as aggregation and infrequent update intervals, questioning its usefulness in electricity distribution network planning. This dissertation studies the use of public sector information in knowledge discovery for electricity distribution network planning, by means of data mining. Three case studies have been conducted. The adoption of plug-in electric vehicles is investigated in descriptive analysis and predictive modeling cases. Prediction of small area load profiles, considering area-specific characteristics, are studied in spatial load forecasting case. In the studies, smart meter readings and public sector information covering census, building, and vehicle registration data are utilized. The contributions of this dissertation include the analysis between area level socio-economics and plug-in electric vehicle adoption, in which the average income of households was found to be the main explanatory factor. The knowledge derived assists in directing development efforts to network areas which may suffer in the future from increased load due to vehicle charging. Another contribution is the developed data mining approach for predicting small area level load profiles using external data, which is novel in this context. The characteristics dealing with age, education and occupation were found to be the explanatory factors providing the best trade-off between the model simplicity and accuracy. The model can estimate loads in areas having distinct building and socio-economic compositions. On a wider scope, the research carried out contributes by addressing some of the common challenges of public sector information in this context and by proposing solutions to deal with them. Clustering based aggregation has been proposed to discover an aggregate level pattern predictable by the more imprecise public sector information, while minimizing the loss of precision. The approach additionally helps in mitigating data quality issues. Valid predictions were achieved in the case studies by using the approach. As the demand for better reliability at low cost is growing, distribution system operators must find new ways to improve their efficiency. To improve and optimize decisions and performance, data driven approaches utilizing large amounts of