Applications of artificial neural networks for time series data analysis in energy domain

Abstract: With the development of artificial intelligence techniques and increased installation of smart meters in recent years, time series analysis using historical data in the energy domain becomes applicable. In this thesis, microdata analysis approaches are used, which consist of data acquisition, data processing, data analysis and data modelling, aiming to address two research problems in the energy domain. The first research problem is short-term electricity price forecasting of a deregulated market and the second one is anomaly detection of heat energy usage in district heating substations.As a result of electricity market deregulation, third party suppliers can enter the market and consumers are free to choose electricity suppliers, which leads to a more transparent and competitive market. Accurate short-term electricity price forecasting is crucial to the market participants in terms of maximizing profits, risk management and other short-term market operations. Literature review is performed aiming to identify the suitable methods. It is concluded that long short-term memory (LSTM) based methods are superior to other methods for time series analysis. Since the gating mechanisms of long short-term memory alleviate the problem of gradient vanishing. Another conclusion form the literature is that hybrid approach that consists of two or more artificial intelligence algorithms complimenting each other is more effective to solve complex real world problem. Based on the conclusions derived, a hybrid approach based on bidirectional LSTM (BDLSTM) and Catboost is proposed for short-term electricity price forecasting of NordPool. Performance of support vector regression (SVR), ARIMA, ensemble tree, multi-layer perception (MLP), gated recurrent unit (GRU), BDLSTM and LSTM are evaluated. Experiment results show that BDLSTM outperforms the other models in terms of Mean percentage error (MAPE), root mean square error (RMSE) and mean absolute error (MAE).Statistics show that market shares of district heating have increased steadily in the past five decades. District heating shares approximately 55% of the heat supply market in Sweden. Therefore, energy efficiency of district heating systems is of great interest to energy stakeholders. Anomalies are rare observations deviated significantly from the majority of the data, and such suspicious observations are important indicators of potential faults. To reduce the financial loss and improve energy efficiency, detecting anomalies from meter readings is essential. Another type of neural network architecture, LSTM variational autoencoder (LSTMVAE) combined with a heat signature model is proposed for anomaly detection using the dataset from an anonymous substation in Sweden. Results show that the proposed method outperforms other two baseline models LSTM, LSTM autoencoder (LSTMAE) in terms of F1 score and AUC.In this thesis, various approaches based on neural networks are explored to solve different time series data analysis in the energy domain, aiming for supporting decision makings of market participants to maximize profits, enhancing risk managements and improving energy efficiency. Although, two problems domains are covered, methods reviewed and applied in the thesis can be tailored for other energy time series analysis problems as well.