###### Abstract  
The intensive industrial development in special economic zones, such as Thailand's Eastern Economic Corridor, increases energy consumption, leading to an imbalance of energy supply and a challenge for energy management. Electricity consumption at a local level is crucial for utility planners to manage and invest in the electrical grid. With this study, we propose an electricity consumption estimation model at the district level using machine learning with publicly available statistical data and built-up area (BU), area of lit (AL), and sum of light intensity (SL) data extracted from Landsat 8 and Suomi NPP satellite nighttime light images. The models created from three machine learning algorithms, which included Multiple Linear Regression (MR), Decision Tree (DT), and Support Vector Regression (SVR), were compared. The results show that (1) electricity consumption is highly correlated with SL, AL, and BU; and (2) the DT model demonstrated a better performance in predicting local electricity consumption when compared to MR and SVR with the lowest error rate and highest R\({}^{2}\). The local government in developing countries with limited data and financial resources can adopt the proposed approach to benefit from utilizing commonly available remote sensing and statistical data with simple machine learning models such as DT (regression method) for sustainable electricity management. e 2021 12 November 2021 18 November  
indicator such as electricity demand [9], household electrification [10], and monitoring of electricity consumption at different spatial and temporal scales in developing (low-middle income) countries, such as China, India, and others [11]. NTL was founded to be an essential indicator of human activities and settlements (urbanization) in spatial dimensions [12]. In addition, spatial information, as well as land-use types, were also utilized to classify the consumer types for forecasting energy consumption when a history of consumption was not available [13]. [PERSON] et al. [6] proposed to use socio-demographic information (demographic, economic, and sociological statistics at a local scale) as an essential supplement for spatially estimating electrical consumption from source zones to target zones with no or little historical consumption data. Various prediction techniques have been used to estimate electricity consumption with the aforementioned proxies such as Linear Regression, Spatial Regression, and Machine Learning Algorithms (MLAs). MLA-based prediction models can identify patterns and react to changes in time series data which are essential for decision makers to understand trends. The models are trained and re-trained to generate a final model that builds upon optimizing the desired objective to predict its output. Moreover, MLAs are suited for complex problems because they emphasize model testing and adjusting [14]. [PERSON] and [PERSON] [15] compared Regression Analysis with two MLAs, including Decision Tree (DT) and Neural Network (NN), for forecasting the energy consumption of data centers based on weather conditions. They concluded that DT and NN models performed slightly better than traditional regression models and more complex predictors. In addition, Least Squares Support Vector Machines (LS-SVM) were suggested for accurate and quick prediction of electricity consumption in Turkey [16]. In a study conducted in France, the Spatial-Temporal Model provided excellent performance when estimating electricity consumption at a small temporal scale with socio-demographic and client information [6]. Additionally, [PERSON] et al. [17] developed a Spatial-Temporal Geographically Weighted Regression Model to simulate electricity consumption in China at the provincial level and achieved goodness of fit of 99% and relative error of less than 5%. The MLAs with a linear regression approach showed satisfactory accuracy in energy consumption forecasting at data centers based on weather information from remotely sensed data [18].