中圖分類(lèi)號(hào)： TN911
文獻(xiàn)標(biāo)識(shí)碼： A
DOI：10.16157/j.issn.0258-7998.211760
中文引用格式： 蔡雨露，聶玉虎，崔文朋，等. 基于諧波分量與有效值的神經(jīng)網(wǎng)絡(luò)負(fù)荷分解[J].電子技術(shù)應(yīng)用，2022，48(8)：123-126.
英文引用格式： Cai Yulu，Nie Yuhu，Cui Wenpeng，et al. Non-intrusive residential electricity load disaggregation based on harmonic components and effective value[J]. Application of Electronic Technique，2022，48(8)：123-126.

Non-intrusive residential electricity load disaggregation based on harmonic components and effective value

Abstract： Non-intrusive load decomposition can decompose the electricity consumption information of each consumer from the current change information of the main meter, which is convenient for providing electricity consumers with more refined and targeted electricity management and dispatching services. The current non-intrusive load decomposition algorithm using one-dimensional convolution has the problems that the decomposition accuracy is not high, the new user appliances need to be retrained, and the complexity is high. Based on this, this paper uses the effective value of current and the harmonic component information after Fourier transform to propose a load decomposition algorithm based on one-dimensional convolutional neural network, which uses similarity comparison to decompose the current information of each consumer, and solves the new problem that increasing users or using electrical appliances requires retraining. It is found through experiments that the method proposed in this paper can also improve the accuracy of load decomposition to a certain extent, and the complexity is low.

Key words : non-intrusive load decomposition；convolutional neural network；smart gridword