Non-Intrusive Load Disaggregation of Industrial Cooling Demand with LSTM Neural Network

As the telecommunication industry becomes more and more energy intensive, energy efficiency actions are crucial and urgent measures to achieve energy savings. The main contribution to the energy demand of buildings devoted to the operation of the telecommunication network is cooling. The main issue in order to assess the impact of cooling equipment energy consumption to support energy managers with awareness over the buildings energy outlook is the lack of monitoring devices providing disaggregated load measurements. This work proposes a Non-Intrusive Load Disaggregation (NILD) tool that exploits a literature-based decomposition with an innovative LSTM Neural Network-based decomposition algorithm to assess cooling demand. The proposed methodology has been employed to analyze a real-case dataset containing aggregated load profiles from around sixty telecommunication buildings, resulting in accurate, compliant, and meaningful outcomes.

Automated summary

As the telecommunication industry consumes more energy, energy efficiency actions are crucial. This study proposes a Non-Intrusive Load Disaggregation tool that uses an LSTM Neural Network algorithm to assess cooling demand, providing insights to energy managers. The methodology has been proven accurate, compliant, and meaningful in analyzing real-case data.