A Multiple Variable Regression-based Approaches to Long-term Electricity Demand Forecasting

다국어 초록 (Multilingual Abstract)

Electricity contributes to the development of the economy. Therefore, forecasting electricity demand plays an important role in the development of the electricity industry in particular and the economy in general. This study aims to provide a precise model for long-term electricity demand forecast in the residential sector by using three independent variables include: Population, Electricity price, Average annual income per capita; and the dependent variable is yearly electricity consumption. Based on the support of Multiple variable regression, the proposed method established a model with variables that relate to the forecast by ignoring variables that do not affect lead to forecasting errors. The proposed forecasting model was validated using historical data from Vietnam in the period 2013 and 2020. To illustrate the application of the proposed methodology, we presents a five-year demand forecast for the residential sector in Vietnam. When demand forecasts are performed using the predicted variables, the R square value measures model fit is up to 99.6% and overall accuracy (MAPE) of around 0.92% is obtained over the period 2018-2020. The proposed model indicates the population's impact on total national electricity demand.

목차 (Table of Contents)

• Abstract
• 1. Introduction
• 2. Problem Definition
• A. Choose a forecasting method
• B. Select variables
• 3. Building Model
• 4. Experiments and Results
• 5. Conclusion
• Acknowledgments
• References