Practical limits to the use of non-intrusive load monitoring in commercial buildings

Meier, Alan; Cautley, Dan

doi:10.1016/j.enbuild.2021.111308

Cited by 25 publications

(14 citation statements)

References 14 publications

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“…The state of the art in terms of methodology and datasets for low-frequency NILM based on deep neural networks is summarised in a recent review [6], and shows that there has been significant progress in designing NILM methods for residential buildings and that data sampling intervals below 10 seconds, large field of view, usage of GAN losses, and post-processing, provide the most accurate results (in terms of classification and estimation accuracy). However, a recent review for NILM in commercial buildings [7], indicates that progress in commercial NILM, at any sampling rate, is slow because of the following challenges arising in non-residential buildings. In the commercial setting, there is usually large number of loads, with widely varying wattage and duty cycle, many of which tend to operate continuously.…”

Section: Background On Nilm For Dairy Farmsmentioning

confidence: 99%

Quantification of Dairy Farm Energy Consumption to Support the Transition to Sustainable Farming

Todic

Stanković

et al. 2022

2022 IEEE International Conference on Smart Computing (SMARTCOMP)

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As the need for using energy-efficient machinery escalates, energy consumption estimation plays an important role in decision support and planning in the agri-sector. Within the present research study, energy consumption in dairy farms was examined. A deep learning-based load disaggregation approach was used to develop data-driven models to quantify individual energy consumption of milk production-related devices of dairy farms, from a single aggregate measurement. According to the experiments conducted on three dairy farms in Germany, load disaggregation from a single aggregate meter is a viable, cheaper alternative to submetering multiple pieces of equipment to accurately quantify electricity consumption at scale in dairy farms in order to provide the decision support needed to inform measures for tackling climate change.

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Section: Background On Nilm For Dairy Farmsmentioning

confidence: 99%

Quantification of Dairy Farm Energy Consumption to Support the Transition to Sustainable Farming

Todic

Stanković

et al. 2022

2022 IEEE International Conference on Smart Computing (SMARTCOMP)

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“…Demand response often requires acquiring multiple loads simultaneously. However, due to the large number and variety of devices used by commercial customers, the approach of training a separate model for each load is no longer applicable [ 21 ]. 2.…”

Section: Introductionmentioning

confidence: 99%

“…Under different power consumption scenarios, different types of devices have different startup or shutdown times. For example, some devices are turned off for a long time and only turned on for a short time, which affects the monitoring accuracy [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Non-Intrusive Commercial Load Monitoring

Zhou

Shao

Wang

et al. 2022

Sensors

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Commercial load is an essential demand-side resource. Monitoring commercial loads helps not only commercial customers understand their energy usage to improve energy efficiency but also helps electric utilities develop demand-side management strategies to ensure stable operation of the power system. However, existing non-intrusive methods cannot monitor multiple commercial loads simultaneously and do not consider the high correlation and severe imbalance among commercial loads. Therefore, this paper proposes a deep learning-based non-intrusive commercial load monitoring method to solve these problems. The method takes the total power signal of the commercial building as input and directly determines the state and power consumption of several specific appliances. The key elements of the method are a new neural network structure called TTRNet and a new loss function called MLFL. TTRNet is a multi-label classification model that can autonomously learn correlation information through its unique network structure. MLFL is a loss function specifically designed for multi-label classification tasks, which solves the imbalance problem and improves the monitoring accuracy for challenging loads. To validate the proposed method, experiments are performed separately in seen and unseen scenarios using a public dataset. In the seen scenario, the method achieves an average F1 score of 0.957, which is 7.77% better than existing multi-label classification methods. In the unseen scenario, the average F1 score is 0.904, which is 1.92% better than existing methods. The experimental results show that the method proposed in this paper is both effective and practical.

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“…Recognition of flexible loads is essential for managing energy systems of factories and industrial parks. However, the researchers are mostly focused on residential and commercial loads (Verma et al, 2021;Azizi et al, 2021;Meier and Cautley, 2021;Brucke et al, 2021). The nonintrusive decomposition and identification for high energy-consuming industrial loads lack in-depth researches, and the researches on industrial park loads (IPL) are even less.…”

Section: Introductionmentioning

confidence: 99%

A robust approach for the decomposition of high-energy-consuming industrial loads with deep learning

Cui

Jin

et al. 2022

Journal of Cleaner Production

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Practical limits to the use of non-intrusive load monitoring in commercial buildings

Cited by 25 publications

References 14 publications

Quantification of Dairy Farm Energy Consumption to Support the Transition to Sustainable Farming

Quantification of Dairy Farm Energy Consumption to Support the Transition to Sustainable Farming

Deep Learning-Based Non-Intrusive Commercial Load Monitoring

A robust approach for the decomposition of high-energy-consuming industrial loads with deep learning

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