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

Eiraudo, Simone; Barbierato, Luca; Giannantonio, Roberta; Patti, Edoardo; Lanzini, Andrea; Bottaccioli, Lorenzo

doi:10.1109/eeeic/icpseurope54979.2022.9854581

Cited by 2 publications

(1 citation statement)

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“…3: Visualization of the original YLP, the trend and the 168 time steps long RPC obtained by additive decomposition of weekly periodicity, whose relevancy is heavily depending on the buildings' usage. Indeed, in pure TLC buildings, electrical demand is strongly dependent on daily and seasonal outdoor temperature fluctuations, since the most important contribution to electrical demand variation is cooling load E CLC [40]. On the contrary, offices load profiles are more strictly affected by E AU X , which in turn heavily depends on the occupancy schedule.…”

Section: A Pre-processing Resultsmentioning

confidence: 99%

A Machine Learning Based Methodology for Load Profiles Clustering and Non-Residential Buildings Benchmarking

Eiraudo

Barbierato

Giannantonio

et al. 2023

IEEE Trans. on Ind. Applicat.

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Buildings benchmarking based on their electric profiles is a fundamental step to identify, evaluate and then possibly implement energy efficiency oriented actions. Indeed, benchmarking enables comparison among peer buildings or industrial sites and the identification of reference cases, either efficient and inefficient ones. In this regard, temporal data clustering is an effective and widely applicable benchmarking tool. In this work, we propose a novel Machine Learning based methodology, taking advantage of two fundamental tools, namely a decomposition algorithm and a clustering one. Several clustering algorithms have been tested to identify k-Means as the most suitable one. The proposed methodology includes the evaluation of energy Key Performance Indicators for effective analysis and comparison of buildings. The proposed framework has been tested on a real-world case study including around 2000 non-residential buildings. The classification of buildings based on K-Means achieved an accuracy of 99.7% with respect to their usage category. Furthermore, reference Key Performance Indicator values for each cluster are obtained and discussed to understand buildings' energy behaviour and possible reasons for inefficiencies.

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Section: A Pre-processing Resultsmentioning

confidence: 99%