Mr. Milutin Pavicevic just defended his Master thesis titled: “Investigation of neural network efficiency in prediction electricity prices in the day-ahead market”. The work focused on the use of artificial intelligence and exploration of various prediction models based on neural networks in order to improve prediction of electricity prices.
ABSTRACT – The power of neural networks in approximating continuous functions has led to more widespread use of this type of artificial intelligence in the field of time-series forecasting. This work examines the efficiency oftime-series prediction models when given the dataset of hourly values connected to day-ahead market of electrical energy. It presents the processing and windowing of the data to fit the prediction models, describes the specifics of the day-ahead market of electrical energy and more closely describes the way each of the used neural network models works.
The work looks at created neural network models with dense layers, convolutional neural networks (CNN) and recurrent neural networks (LSTM), and measures their performance. Testing results show their accuracy when predicting based on the dataset of hourly values of day-ahead electricity on the HUPX market, coupled with the hourly weather data, as well as the related dataset of the hourly values of electricity consumption in Montenegro.
