Resident Load Influence Analysis Method for Price Based on Non-intrusive Load Monitoring and Decomposition Data

Jiang, Wenqian; Zeng, Bo; Zhou, Yang; Li, Gang

doi:10.1088/1755-1315/108/5/052047

Cited by 2 publications

(1 citation statement)

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“…Therefore, accurate and efficient classification of household electricity consumption (60% of total electricity generation) can guide household users' electricity consumption habits, improve energy efficiency utilization, and reduce energy consumption by at least 9% [3]. The use of nonintrusive load monitoring (NILM) methods is significant in improving the overall efficiency of the grid, enabling accurate short-term load forecasting [4], optimizing peak and valley tariffs [5], and achieving development goals such as affordable clean energy, industry, innovation and infrastructure, and sustainability [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

An intelligent non-intrusive load monitoring model based on power encoding and convolutional state modules

Xu,

Jiang,

Zhang

et al. 2024

Meas. Sci. Technol.

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Non-Intrusive Load Monitoring (NILM) identifies device power consumption or on/off states solely based on total power data, which is highly valuable for consumers to understand their appliance usage behavior and take necessary measures to reduce energy consumption, especially for the benefit of energy consumers' living production. However, a challenge faced by NILM is the tendency to focus excessively on power disaggregation while neglecting the disaggregation of on/off states, leading to lower classification accuracy, particularly owning to imbalanced states. This study proposes a model that integrates the power and on/off states to simultaneously disaggregate the power and device on/off states. The model comprises two main modules: a power encoding module for power disaggregation, and a convolutional state module for on/off state disaggregation. The power encoding module utilizes BERT-LSTM and Long Short-Term Memory (LSTM) networks for initial energy disaggregation. In contrast, the convolutional state module employs Convolutional Neural Networks (CNNs) for device state disaggregation. The output of the power-encoding module is multiplied by the probability of on/off states to obtain the final power.The proposed model is evaluated using the REDD and UK-DALE datasets. Compared to the baseline models, the results show an improvement in the device state classification average accuracy from 0.948 to 0.957, and a decrease in the average error between the real power and disaggregated power from 26.356W to 25.108W. Additionally, real-world experiments conducted using the designed platform for collecting and disaggregating power data achieve an average accuracy of 0.997. The proposed model demonstrates competitiveness in the NILM field and underscores its significance in aiding energy-consumption reduction efforts.

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