As China’s distributed energy is still in the development stage, energy transmission loss will inevitably occur in the transmission process from the source end to the load end. To reduce transmission energy loss, we should also beware of electricity theft. The principle of common electricity theft methods is analyzed to improve the accuracy of established electricity theft characteristics and electricity theft detection. The ReliefF multivariate characteristics selection algorithm optimizes the electricity theft characteristics. The back propagation (BP) neural network-based electricity theft detection model is built, and the optimized characteristics are selected as the model’s input. The experiment results show that the detection model has better electricity theft identification accuracy using the optimized characteristics for electricity theft detection.

由于我国分布式能源仍处于发展阶段，从源端到负荷端的传输过程中必然会出现能量传输损耗。为了减少传输能量损失，我们还应该提防偷电。分析了常用窃电方法的原理，以提高建立的窃电特征和窃电检测的准确性。ReliefF 多元特性选择算法优化了窃电特性。建立了基于反向传播（BP）神经网络的窃电检测模型，并选择优化后的特征作为模型的输入。