Abstract
It is very difficult to predict the Maximum Power Demand (MPD) of customers in high performance because of various factors. In this paper, the problem of MPD prediction is studied by using fused machine learning algorithms. Firstly, an improved grey relation analysis method is adopted to analyze relevant influencing factors. Secondly, a modified prediction algorithm based on an adaptive cubature Kalman filter combined with Fbprophet is proposed according to the characteristics of customers' MPD. Finally, the proposed algorithm of this paper is applied to predict MPD and cost is evaluated. Experiment results show that the improved MPD prediction algorithm can comprehensively consider the relevant factors, and has good performance in time series prediction.
Highlights
Since the beginning of the 21st century, applications of modern control theory have entered a prosperous period
This paper focuses on the core problems, including load characteristics, selection of main influencing factors and construction of high-precision forecasting model, of Maximum Power Demand (MPD) forecasting for large industrial users
The proposed solution includes the following steps: firstly, collect and analyze relevant policy information, and determine a prediction model according to the load characteristics; secondly, because Fbprophet algorithm has a good effect in modeling data with characteristics of segmented trend and multi-cycle, it is suitable for predict the MPD of power enterprises; thirdly, the influencing factors are analyzed from multiple perspectives
Summary
Since the beginning of the 21st century, applications of modern control theory have entered a prosperous period. Large industrial consumers take electrical energy mainly for industrial production Their electrical characteristics and prediction process of MPD will be affected by many factors, such as weather conditions, temperature, wind rating, power policy, production planning, etc. The proposed solution includes the following steps: firstly, collect and analyze relevant policy information, and determine a prediction model according to the load characteristics; secondly, because Fbprophet algorithm has a good effect in modeling data with characteristics of segmented trend and multi-cycle, it is suitable for predict the MPD of power enterprises; thirdly, the influencing factors are analyzed from multiple perspectives. Information about the consumer’s production plan is collected, and an analysis method based on improved GRA [20] is adopted to analyze the relevant factors influencing the MPD. All the above factors will interfere with the MPD and affect the prediction performance
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