Abstract
Non-intrusive load monitoring (NILM) is a process of estimating operational states and power consumption of individual appliances, which if implemented in real-time, can provide actionable feedback in terms of energy usage and personalized recommendations to consumers. Intelligent disaggregation algorithms such as deep neural networks can fulfill this objective if they possess high estimation accuracy and lowest generalization error. In order to achieve these two goals, this paper presents a disaggregation algorithm based on a deep recurrent neural network using multi-feature input space and post-processing. First, the mutual information method was used to select electrical parameters that had the most influence on the power consumption of each target appliance. Second, selected steady-state parameters based multi-feature input space (MFS) was used to train the 4-layered bidirectional long short-term memory (LSTM) model for each target appliance. Finally, a post-processing technique was used at the disaggregation stage to eliminate irrelevant predicted sequences, enhancing the classification and estimation accuracy of the algorithm. A comprehensive evaluation was conducted on 1-Hz sampled UKDALE and ECO datasets in a noised scenario with seen and unseen test cases. Performance evaluation showed that the MFS-LSTM algorithm is computationally efficient, scalable, and possesses better estimation accuracy in a noised scenario, and generalized to unseen loads as compared to benchmark algorithms. Presented results proved that the proposed algorithm fulfills practical application requirements and can be deployed in real-time.
Highlights
Energy conservation in residential and commercial buildings through smart electrification has been a hot topic for researchers in recent years [1,2]
Fridge, and washing machine data were obtained from house-1, whereas dishwasher, electric stove, and television data were retrieved from house-2 of the Electricity Consumption and Occupancy (ECO) dataset
The ultimate goal of a non-intrusive load monitoring (NILM) solution is to apply it in real-time, which is possible if the disaggregation algorithm fulfills practical application requirements
Summary
Energy conservation in residential and commercial buildings through smart electrification has been a hot topic for researchers in recent years [1,2]. [31] and He [29] proposed a LSTM based deep neural network architecture and trained it using 6-sec sampled data with only active power as an input feature. Their LSTM based DNN model was unable to identify multi-state appliances. To achieve high estimation accuracy and lowest generalization error with a limited amount of data, this paper proposes a three-stage disaggregation algorithm based on a deep LSTM network.
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