KEYWORDS: Data modeling, Data acquisition, Feature extraction, Convolution, Telecommunications, Electrical engineering, Data conversion, Convolutional neural networks, Data fusion, Evolutionary algorithms
In order to solve the problem of inaccurate force load prediction results, a power load prediction method is proposed.By acquiring the historical power load data of different monitoring points, maximize the minimum normalization of the historical power load data, construct the power load input matrix based on the normalized power load data, construct the convolutional neural network to extract the spatial features, construct the gated cycle unit to introduce the attention mechanism, input the extracted spatial features to extract the time features, construct the periodic features of the power load data, and integrate all features for prediction.
In order to realize the position detection of false data injection, a position detection model based on bi-directional gated cycle unit optimized full convolution neural network is proposed. In view of the problem that the traditional false data injection detection is limited to judging whether the attack occurs or not, the processing of multi-level detection is proposed to realize the fine discrimination of the attacked data and effectively avoid the resource loss caused by direct location detection. According to the time series characteristics of the measured data, the bi-directional gated cycle unit is applied to the convolution layers of the full convolution neural network, which can effectively extract the spatiotemporal features between the data and improve the accuracy and efficiency of false data injection detection. The experimental results show that the optimized position detection model has a certain improvement in detection accuracy and efficiency compared with the traditional convolution neural network.
KEYWORDS: Solar energy, Particles, Data modeling, Photovoltaics, Information technology, Solar cells, Optimization (mathematics), Power supplies, Platinum, Optical power tracking algorithms
With the maturity of home load control technology and smart grid technology, optimal scheduling of household microgrid has become an important means to realize personalized and differentiated electricity service for customers. To address the problems of load diversity of household microgrid and satisfaction of customer-side participation in demand response, we combine the theory of customer-side demand response with the idea of data mining to carry out research. Firstly, a multiattribute demand response strategy model based on satisfaction is established with the maximum customer satisfaction index as the objective function, and electricity consumption behavior habits are mined from the historical electricity consumption data of customers. Second, the improved particle swarm algorithm is used to solve the dispatching model. The results verify the effectiveness of the proposed model and solution algorithm, which can help customers to reduce electricity costs while ensuring customer satisfaction and reducing the peak-to-valley difference of the grid.
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