###### Abstract
Furthermore, the new hybrid Convolutional Neural Network (CNN) model illustrated excellent performance in forecasting residential and commercial buildings' electricity consumption estimation [19]. [PERSON] et al. [20] integrated the nighttime light data with the spatial econometric model to quantify energy consumption in China at a regional level. Their result illustrated that the Spatial Durbin Model (SDM) provided high accuracy of electricity consumption at provincial and prefectural scales. However, these previous studies focused on estimating the electricity consumption either at a very coarse country level or a very detailed property scale [21]. The countrywide value is impractical for local management, while the fine-scale value requires intensive inputs that are not readily available in many developing nations. Therefore, research on estimating the electricity consumption at a medium scale (district level) remains limited but should be pursued to support the local electricity supply management and quantify the socio-economic impact of intensive industrial development policy (i.e., human well-being). Thailand's Eastern Economic Corridor (EEC) includes Rayong, Chonburi, and Chachoengsao provinces (Figure 1) and is a particular example representing how the developing country use policy strategy to drive regional development. The EEC development policy aims to create opportunities for investors to participate in developing utilities, infrastructure, and public transportation. Thailand's government expected that EEC development would raise the Gross Provincial Product (GPP) and the country's economy and improve the quality of life of people who lives in the vicinity by connecting the emerging global economy. With the country's rapid development and intensive promotion of the industrial economy, the demand for electricity consumption has increased. The total electricity consumption in the domestic sector rose from 25,525 GWh in 2012 to 29,074 GWh in 2018 [22]. Concurrently, Thailand must diversify the domestic energy source, improve energy efficiency, and promote community participation in energy management, following Sustainable Development Goal (SDG) 7 to ensure access to affordable, reliable, sustainable, and modern energy for all. Moreover, the local governments of the key district (Rayong, Chonburi, and Chachoengsao provinces) play an essential role in managing the limited resources of electricity to ensure the EEC's sustainable growth. The EEC's electricity consumption is heterogeneous due to the differences in geographic and socioeconomic factors. Rayong and Chonburi are the top GPP generators in Thailand because of their early industrial and tourism development from the Eastern Seaboard policy in 1990. Conversely, Chachoengsao has different economic activities, mainly focusing on agricultural and cultural tourism with medium GPP. Therefore, the electricity consumption estimation at a medium scale (district level) is required to support local government decision making. In 2015, [PERSON] et al. forecasted electricity demand in Chonburi province based on classified land-use types from Landsat 5 and 7. They used the built-up area to estimate the electricity demand per square kilometer. Their result showed that a medium correlation of 0.47 existed between predicted electricity demand and actual electricity consumption [23]. The electricity consumption in the districts close to industrial and tourism development areas was underestimated. Using Thailand's EEC as a case study, this paper aimed to build a suitable estimation model for electricity consumption at the district level by employing and comparing three MLAs (MR, DT, and SVM). Due to the limitation of the electricity load and electricity consumption database within the study area, we extracted nighttime light and built-up area from the remotely sensed data. We used them along with general socioeconomic data, such as population, household, and  
Figure 1: The study site (Eastern Economic Corridor, Thailand).