Abstract
In this paper, a short-term power load forecasting model based on feature similarity was proposed. The model can comprehensively consider the short-term load forecasting under the condition of multiple factors, such as meteorological information, holiday/workday information and other factors in a unified framework. Different values of the same characteristic have great influence on load forecasting. Therefore, hierarchical clustering algorithm is used to analyse the value of each feature dimension. Feature distance is used as feature mapping value to establish feature mapping relation table. Because different factors have different weights for load forecasting, a weighted feature similarity measurement strategy is designed. Taking the minimum residual sum of squares as the optimization objective, particle swarm optimization algorithm is used to solve the optimal characteristic weight. The validity of the model and the accuracy of load forecasting are verified by comparing the numerical simulation with the existing models.
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