Spatial distribution characteristics of community service facilities in Shangcheng District of Hangzhou, China

Shi, Jianren; Du, Xiaqin

doi:10.1117/12.3004072

Cited by 1 publication

(1 citation statement)

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“…As shown in Figure 3 below, for a user within 7 days of the total power consumed by household electrical appliances and the power consumed by each electrical appliance individually, the total power is superimposed by the power consumed by each individual electrical appliance. The essence of the NILM algorithm is actually based on the identification of different loads during steady state operation or the switching operating state when the different electrical features can be classified into steady state features and transient features, which are also divided into non-traditional features in [15,22]. The commonly used load feature library classification and feature extraction methods are shown in Table 1 below.…”

Section: Load Characteristicsmentioning

confidence: 99%

Non-Intrusive Load Monitoring Based on Multiscale Attention Mechanisms

Yao,

Wang,

Zhao

2024

Energies

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With the development of smart grids and new power systems, the combination of non-intrusive load identification technology and smart home technology can provide users with the operating conditions of home appliances and equipment, thus reducing home energy loss and improving users’ ability to demand a response. This paper proposes a non-intrusive load decomposition model with a parallel multiscale attention mechanism (PMAM). The model can extract both local and global feature information and fuse it through a parallel multiscale network. This improves the attention mechanism’s ability to capture feature information over long time periods. To validate the model’s decomposition ability, we combined the PMAM model with four benchmark models: the Long Short-Term Memory (LSTM) recurrent neural network model, the Time Pooling-based Load Disaggregation Model (TPNILM), the Extreme Learning Machine (ELM), and the Load Disaggregation Model without Parallel Multi-scalar Attention Mechanisms (UNPMAM). The model was trained on the publicly available UK-DALE dataset and tested. The models’ test results were quantitatively evaluated using a confusion matrix. This involved calculating the F1 score of the load decomposition. A higher F1 score indicates better model decomposition performance. The results indicate that the PMAM model proposed in this paper maintains an F1 score above 0.9 for the decomposition of three types of electrical equipment under the same household user, which is 3% higher than that of the other benchmark models on average. In the cross-household test, the PMAM also demonstrated a better decomposition ability, with the F1 score maintained above 0.85, and the mean absolute error (MAE) decreased by 5.3% on average compared with that of the UNPMAM.

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Section: Load Characteristicsmentioning

confidence: 99%