NILM based Energy Disaggregation Algorithm for Dairy Farms

Yadav, Akhilesh Kumar; Sinha, Anuj; Saidi, Abdessamad; Trinkl, Christoph; Zörner, Wilfried

doi:10.1145/3427771.3427846

Cited by 11 publications

(6 citation statements)

References 6 publications

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“…Unlike existing frameworks, this method can disaggregate loads and detection appliance load status simultaneously. In Reference [98], appliance identification is conducted by disaggregating electric loads in dairy farms using a multilayer DL network based on the sequence-to-sequence (S2S), and (ii) a 1D-CNN based-bidirectional GRU (BiGRU) network. Moving forward, a load disaggregation with attention-based DNN (LDwA-DNN) is proposed in Reference [99].…”

Section: Learning Models (L)mentioning

confidence: 99%

Recent trends of smart nonintrusive load monitoring in buildings: A review, open challenges, and future directions

Himeur

Alsalemi

Bensaali

et al. 2022

Int J of Intelligent Sys

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Smart nonintrusive load monitoring (NILM) represents a cost-efficient technology for observing power usage in buildings. It tackles several challenges in transitioning into a more effective, sustainable, and digital energy efficiency environment. This paper presents a comprehensive review of recent trends in the NILM field, in which we propose a multiperspective classification of existing smart NILM techniques. More attention is devoted to describing the contributions of deep learning, feature extraction, computing platforms, and application scenarios for NILM development. Accordingly, NILM technical aspects are first Int J Intell Syst. 2022;37:7124-7179. wileyonlinelibrary.com/journal/int investigated, including data collection devices and public data sets. Next, event-based and non-eventbased NILM algorithms are overviewed. Furthermore, potential limitations of existing solutions are identified, highlighting their technical challenges, especially those related to security and privacy preservation, data scarcity, results reproduction, and implementation and business difficulties. Lastly, future directions are explored to overcome the identified limitations.

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Section: Learning Models (L)mentioning

confidence: 99%

Recent trends of smart nonintrusive load monitoring in buildings: A review, open challenges, and future directions

Himeur

Alsalemi

Bensaali

et al. 2022

Int J of Intelligent Sys

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“…Identification of on/off states of the machines resulted in classification accuracy of F 1 -Score of 0.93 ± 0.07 for WaveNILM and 0.79 ± 0.12 for FHMM. In [4], two different approaches for NILM classification are compared, one using LSTM model, and the other using One-Directional Convolution Layer-Bidirectional GRU Recurrent Neural Network, to detect milk cooling and vacuum pump, for two dairy farms in Germany. Unfortunately, the dataset was not made public.…”

Section: Background On Nilm For Dairy Farmsmentioning

confidence: 99%

“…There are fewer studies that focus on cheaper and low frequency sampling (less than 1 Hz), that is akin to national smart meter rollouts. To the best of our knowledge, there has only been one attempt at leveraging on NILM classification, without quantification of energy consumption of milk production equipment, for dairy farms [4], [5].…”

Section: Introductionmentioning

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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“…Therefore, it only needs to compare the appliance sample that causes the appliance event with the appliance features in the existing feature database and find out the appliance that is most similar to the sample in the feature database, that is, establish the mapping of "single appliance feature-single appliance state" to realize the appliance identification. On the other hand, the NILM based on the energy disaggregation algorithms directly takes the appliance features of aggregated power data samples as input [22]- [24]. Therefore, there are usually two ideas, respectively: (1) Establish the mapping of "bus appliance feature -appliance state combination," that is, find out the appliance state combination that is most similar to the aggregated monitoring sample in the characteristic appliance database and the method of getting the appliance state combination is usually to add the feature data of different appliances; (2) Establish the relationship between "bus appliance feature and single appliance state," treating the NILM problem as a single channel blind source separation problem.…”

Section: Introductionmentioning

confidence: 99%

Non-intrusive load identification based on semi-supervised learning with flexible threshold and V-I trajectory signature

Tang

et al. 2023

Preprint

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Non-intrusive load monitoring (NILM) can obtain fine-grained electricity consumption information for each appliance in the user's home without installing additional hardware sensors. As deep learning has grown quickly, many deep learning algorithms have been used to solve NILM problems, and have achieved good load identification results. However, many supervised learning algorithms need to be based on a large amount of labeled data, which is time-consuming and laborious to obtain, and difficult to implement in actual deployment. In this paper, we propose a semi-supervised learning algorithm that combines consistency regularization and pseudo-labels to help identify appliances with a small amount of labeled data and some unlabeled data. In addition, given the different learning difficulties of different appliances among users, for example, the feature learning difficulty of multi-state appliances is often higher than that of single-state appliances. Therefore, we employ that the threshold values of different appliance classes be changed in a flexible way at each time step so that the informative unlabeled data and their pseudo-labels can be passed. We have carried out simulation experiments on public dataset, and the experimental results show that the proposed algorithm achieves better appliance identification results than the state-of-the-art methods.

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NILM based Energy Disaggregation Algorithm for Dairy Farms

Cited by 11 publications

References 6 publications

Recent trends of smart nonintrusive load monitoring in buildings: A review, open challenges, and future directions

Recent trends of smart nonintrusive load monitoring in buildings: A review, open challenges, and future directions

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

Non-intrusive load identification based on semi-supervised learning with flexible threshold and V-I trajectory signature

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