Forecasting Electricity Consumption of the Local Government Agencies in Ubon Ratchathani Province, Thailand
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This time series forecasting research aimed to forecast the electricity consumption of the local government agencies in Ubon Ratchathani Province and examine the strength of its relationship with temperature. Monthly electricity consumption data for the fiscal years 2022 to 2024 were obtained from the Energy Policy and Planning Office, while climate data from 2014 to 2024 were collected from the Northeastern Meteorological Center (Lower Part). The analysis focused on the five government agencies with the highest electricity consumption, using monthly data from June 2021 to January 2024. The training dataset comprised data from fiscal years 2021 to 2023. Descriptive statistics and correlation analysis were conducted using JAMOVI software, while the Box and Jenkins forecasting method was performed using the R program, package “forecast”, function “auto.arima()”. The results indicated that electricity consumption at Ubon Ratchathani Central Prison, Ubon Ratchathani Technical College, and Public Relations Office District 2 is expected to decrease. In contrast, Ubon Ratchathani Airport and Medical Sciences Center 10, Ubon Ratchathani, are projected to increase. Pearson’s correlation analysis revealed a strong positive relationship between the monthly average daily maximum temperature and electricity consumption, with correlation coefficients ranging from 0.576 to 0.745, except for Ubon Ratchathani Technical College (College), which showed a lower correlation of 0.236. This study explores the increasing demand for precise electricity consumption forecasting in government agencies to support energy efficiency policies. The findings provide valuable insights for developing data-driven energy management strategies by analyzing historical consumption patterns and climatic factors. These results can help policymakers implement effective energy-saving measures, optimize resource allocation, and mitigate the impact of climatic variability.
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