Spatial Electric Load Forecasting Using a Geographic Information System: A Case Study of Khon Kaen, Thailand

Electricity is an indispensable element in daily life and economic development. Hence, load forecasting is a vital part of electricity generation and transmission expansion planning. Although most previous studies in the current literature have focused on quantitative load forecasting using regression or machine learning techniques, they fail to consider complex location-oriented problems of load growth. Therefore, this study presents a spatial load forecasting method using a geographic information system (GIS) via ArcGIS software. A case study is conducted in the urban area of Tessaban Nakhon Khon Kaen, Muang District, Khon Kaen Province, Thailand, which has experienced considerable load growth in recent years. This study focuses on two categories of end-users: residential and business consumers. The forecasting procedures are separated into two parts: quantitative forecasting, achieved using regression methods, and spatial forecasting. Spatial forecasting is achieved by evaluating access to an area causing load growth, considering multiple constraints and evaluation criteria for each end-user. The spatial load forecasting calculation uses four technical evaluation scores: consumer preference, physical accessibility, load availability, and access suitability. Load forecasting maps are created and used to represent the spatial resolution of load growth. The results reveal the trend and direction of the increase in power consumption annually, which can be applied to future electricity grid expansion plans.

I. Introduction

Electricity is an indispensable component of daily life and economic development. Although electricity demand (load growth) slowed significantly in 2022 due to slower global economic growth and higher energy prices following Russia's invasion of Ukraine, global electricity demand was expected to grow by 2.4% in 2022 following the previous year's 6% increase [1]. Load growth can largely be categorized into two events: connecting new consumers and an increase in existing consumer demand [2]. A significant increase in electricity demand places stress on transmission lines or feeders in distribution networks. Hence, distribution system operators (DSOs) should create appropriate plans to manage and make decisions in response to this concern while simultaneously guaranteeing the system's reliability and minimum operating costs [3].