Integration of LSTM based Model to guide short-term energy forecasting for green ICT networks in smart grids

Malik, Hamid; Pouttu, Ari (2022-12-01)
 
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https://doi.org/10.1109/SmartGridComm52983.2022.9960992

Malik, Hamid
Pouttu, Ari
IEEE
01.12.2022

H. Malik and A. Pouttu, "Integration of LSTM based Model to guide short-term energy forecasting for green ICT networks in smart grids," 2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), Singapore, Singapore, 2022, pp. 290-295, doi: 10.1109/SmartGridComm52983.2022.9960992

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doi:https://doi.org/10.1109/SmartGridComm52983.2022.9960992
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https://urn.fi/URN:NBN:fi-fe202301173340
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Abstract

Existing ICT networks are characterized by high level of energy consumption. In order to power up 5G base station sites, rising energy cost and high carbon emissions are major concerns that need to be dealt with. To achieve carbon neutrality, ICT sector needs to transform base station sites in a self-sustainable manner using renewable energy sources, local batteries and energy conservation techniques, even in adverse weather conditions and unexpected power outages. In this paper, short term-forecasting models are studied for accurate energy consumption and production forecast. The proposed architecture provides adaptive energy conservation technique using time series data analysis and Long Short-Term Memory for 5GNR base station site which is independent of traditional power sources and is completely powered by green energy. The accuracy analysis of this study was performed by the Mean Square Error (MSE) and Root Mean Square Error (RMSE). The results show high accuracy levels of LSTM model in guiding short-term energy forecasting for green ICT networks.

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