Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/32573
Title: Analysis and forecasting energy consumption for educational institutions
Authors: Aleksandra Zlatkova
Marija Markovska
Branislav Gerazov
Dimitar Taskovski
Keywords: deep learning models
encoder-decoder
energy consumption
LSTM
time series
Issue Date: Jun-2022
Conference: Leten simpozium za elektronika i obrabotka na signali
Abstract: The energy forecasting is one of the biggest challenges in last years. Nowadays, the problems as energy and economic crises, growth of population, fast development of technology impose the need of energy forecasting. In this paper, three popular deep neural networks: Stacked LSTM, LSTM encoder-decoder and CNN-LSTM encoder-decoder are used for forecasting energy consumption. They are trained and tested on time series that represent the energy consumption of Faculty of Electrical Engineering and Information Technologies in Skopje. The prediction is based on energy consumption in past 24 hours. The proposed models are evaluated with two metrics: RMSE and MAE. All three models show high performance, but LSTM encoder-decoder achieves the highest accuracy.
URI: http://hdl.handle.net/20.500.12188/32573
Appears in Collections:Faculty of Electrical Engineering and Information Technologies: Conference Papers
Faculty of Electrical Engineering and Information Technologies: Conference Papers

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