| Summary: | The purpose of this thesis is to investigate how data from a residential property owner can be utilized to enable better energy management for their building stock. Specifically, this is done through the development of two machine learning models with the objective of detecting anomalies in the existing data of electricity consumption. The dataset consists of two years of residential electricity consumption for 193 substations belonging to the residential property owner Uppsalahem. The first of the developed models uses the K-means method to cluster substations with similar consumption patterns to create electricity profiles, while the second model uses Gaussian process regression to predict electricity consumption of a 24 hour timeframe. The performance of these models is evaluated and the optimal models resulting from this process are implemented to detect anomalies in the electricity consumption data. Two different algorithms for anomaly detection are presented, based on the differing properties of the two earlier models. During the evaluation of the models, it is established that the consumption patterns of the substations display a high variability, making it difficult to accurately model the full dataset. Both models are shown to be able to detect anomalies in the electricity consumption data, but the K-means based anomaly detection model is preferred due to it being faster and more reliable. It is concluded that substation electricity consumption is not ideal for anomaly detection, and that if a model should be implemented, it should likely exclude some of the substations with less regular consumption profiles.
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