Estimating District-Level Electricity Consumption Using Remotely Sensed Data in Eastern Economic Corridor, Thailand
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 mana...
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MDPI AG
2021
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oai:doaj.org-article:26546d66570f4bb4af5b645fef9b5b672021-11-25T18:55:09ZEstimating District-Level Electricity Consumption Using Remotely Sensed Data in Eastern Economic Corridor, Thailand10.3390/rs132246542072-4292https://doaj.org/article/26546d66570f4bb4af5b645fef9b5b672021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4654https://doaj.org/toc/2072-4292The 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<sup>2</sup>. 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.Sirikul HutasaviDongmei ChenMDPI AGarticleelectricity consumptionlocally prediction modelremote sensingdecision treeregression methodmachine learningScienceQENRemote Sensing, Vol 13, Iss 4654, p 4654 (2021) |
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electricity consumption locally prediction model remote sensing decision tree regression method machine learning Science Q |
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electricity consumption locally prediction model remote sensing decision tree regression method machine learning Science Q Sirikul Hutasavi Dongmei Chen Estimating District-Level Electricity Consumption Using Remotely Sensed Data in Eastern Economic Corridor, Thailand |
| description |
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<sup>2</sup>. 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. |
| format |
article |
| author |
Sirikul Hutasavi Dongmei Chen |
| author_facet |
Sirikul Hutasavi Dongmei Chen |
| author_sort |
Sirikul Hutasavi |
| title |
Estimating District-Level Electricity Consumption Using Remotely Sensed Data in Eastern Economic Corridor, Thailand |
| title_short |
Estimating District-Level Electricity Consumption Using Remotely Sensed Data in Eastern Economic Corridor, Thailand |
| title_full |
Estimating District-Level Electricity Consumption Using Remotely Sensed Data in Eastern Economic Corridor, Thailand |
| title_fullStr |
Estimating District-Level Electricity Consumption Using Remotely Sensed Data in Eastern Economic Corridor, Thailand |
| title_full_unstemmed |
Estimating District-Level Electricity Consumption Using Remotely Sensed Data in Eastern Economic Corridor, Thailand |
| title_sort |
estimating district-level electricity consumption using remotely sensed data in eastern economic corridor, thailand |
| publisher |
MDPI AG |
| publishDate |
2021 |
| url |
https://doaj.org/article/26546d66570f4bb4af5b645fef9b5b67 |
| work_keys_str_mv |
AT sirikulhutasavi estimatingdistrictlevelelectricityconsumptionusingremotelysenseddataineasterneconomiccorridorthailand AT dongmeichen estimatingdistrictlevelelectricityconsumptionusingremotelysenseddataineasterneconomiccorridorthailand |
| _version_ |
1718410511921446912 |