Patent ID: 11868869
Assignee: ZHEJIANG LAB
Field: Computer technology (Electrical engineering)
Classification: CPC G  H | IPC G

Claim 2:
3. The non-intrusive load monitoring method based on temporal attention mechanism according to claim 2, wherein, the step 4 is specially as follows:
in step 4.1, inputting Ui to the following deep learning neural networks:

h0=Ui 

hm=Φ(Wm·hm−1+bm)

wherein, h0 is neural network input, hm, Wm, and bm are respectively the output, weight and bias of the mth hidden layer of the neural network model, and Φ(·) is the activation function;
in step 4.2, designing the following output layer for learning:

Fi=Ψ(WM·hM+bM)

wherein, Fi=[{circumflex over (P)}tj:tj+wi, {circumflex over (Q)}tj:tj+wi, Ŝtj:tj+wi] refers to a load forecast of equipment i, hM refers to the output of the last hidden layer of the network, WM and bM refers to the weight and bias of the output layer respectively, Ψ(·) is the activation function;
in step 4.3, the following loss function is designed to train the constructed deep learning neural network model:

lossi=E(Fi, Ltraini)

wherein, E is a prediction deviation measurement function.