{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:32:30Z","timestamp":1760236350568,"version":"build-2065373602"},"reference-count":46,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2021,11,18]],"date-time":"2021-11-18T00:00:00Z","timestamp":1637193600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The intensive industrial development in special economic zones, such as Thailand\u2019s 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 R2. 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.<\/jats:p>","DOI":"10.3390\/rs13224654","type":"journal-article","created":{"date-parts":[[2021,11,19]],"date-time":"2021-11-19T02:43:09Z","timestamp":1637289789000},"page":"4654","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Estimating District-Level Electricity Consumption Using Remotely Sensed Data in Eastern Economic Corridor, Thailand"],"prefix":"10.3390","volume":"13","author":[{"given":"Sirikul","family":"Hutasavi","sequence":"first","affiliation":[{"name":"Laboratory of Geographic Information and Spatial Analysis (LaGISA), Department of Geography and Planning, Queen\u2019s University, Kingston, ON K7L 3N6, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5419-8735","authenticated-orcid":false,"given":"Dongmei","family":"Chen","sequence":"additional","affiliation":[{"name":"Laboratory of Geographic Information and Spatial Analysis (LaGISA), Department of Geography and Planning, Queen\u2019s University, Kingston, ON K7L 3N6, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1016\/j.energy.2019.04.221","article-title":"Modeling electricity consumption using nighttime light images and artificial neural networks","volume":"179","year":"2019","journal-title":"Energy"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1006\/jeem.1998.1056","article-title":"What Drives Deforestation in the Brazilian Amazon? 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