Study on Forecasting Model of Monthly Electricity Consumption Based on Kernel Partial Least-Squares and Exponential Smoothing Method

Abstract:

it is very necessary for electricity market operation to accurate forecasting monthly electricity consumption, influencing factors of electricity consumption, there are non-linear and strong correlation, taking into account the cyclical trend of the monthly electricity consumption, this paper raises a monthly electricity consumption forecast model based on kernel partial least squares and exponential smoothing regression. The forecast model is the first to use kernel partial least squares regression methods to predict the annual electricity consumption, and then combined with exponential smoothing obtained monthly electricity accounts for the proportion of electricity consumption throughout the year for each month of the year to be measured power consumption . Instance analysis and calculation results show that the method has higher prediction accuracy, good practicality and feasibility.

• Periodical: Advanced Materials Research (Volumes 805-806)
• Pages: 1221-1227
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.805-806.1221
• Online since: September 2013
• Authors: Hai Yan Wang, Shi Jun Chen
• Keywords: Electricity Consumption Forecast, Exponential Smoothing Method, Kernel Partial Least-Squares, Monthly Electricity Consumption

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