Platform-Independent Web Application for Short-Term Electric Power Load Forecasting on 33/11 kV Substation Using Regression Tree
Short-term electric power load forecasting is a critical and essential task for utilities in the electric power industry for proper energy trading, which enables the independent system operator to operate the network without any technical and economical issues. From an electric power distribution sy...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-07-01
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| Series: | Computers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-431X/11/8/119 |
| _version_ | 1797432304642031616 |
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| author | Venkataramana Veeramsetty Modem Sai Pavan Kumar Surender Reddy Salkuti |
| author_facet | Venkataramana Veeramsetty Modem Sai Pavan Kumar Surender Reddy Salkuti |
| author_sort | Venkataramana Veeramsetty |
| collection | DOAJ |
| description | Short-term electric power load forecasting is a critical and essential task for utilities in the electric power industry for proper energy trading, which enables the independent system operator to operate the network without any technical and economical issues. From an electric power distribution system point of view, accurate load forecasting is essential for proper planning and operation. In order to build most robust machine learning model to forecast the load with a good accuracy irrespective of weather condition and type of day, features such as the season, temperature, humidity and day-status are incorporated into the data. In this paper, a machine learning model, namely a regression tree, is used to forecast the active power load an hour and one day ahead. Real-time active power load data to train and test the machine learning models are collected from a 33/11 kV substation located in Telangana State, India. Based on the simulation results, it is observed that the regression tree model is able to forecast the load with less error. |
| first_indexed | 2024-03-09T09:58:27Z |
| format | Article |
| id | doaj.art-8af46a06a2714268b368cda20037c697 |
| institution | Directory Open Access Journal |
| issn | 2073-431X |
| language | English |
| last_indexed | 2024-03-09T09:58:27Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj.art-8af46a06a2714268b368cda20037c6972023-12-01T23:34:47ZengMDPI AGComputers2073-431X2022-07-0111811910.3390/computers11080119Platform-Independent Web Application for Short-Term Electric Power Load Forecasting on 33/11 kV Substation Using Regression TreeVenkataramana Veeramsetty0Modem Sai Pavan Kumar1Surender Reddy Salkuti2Center for AI and Deep Learning, Department of Electrical and Electronics Engineering, SR University, Warangal 506371, IndiaDepartment of Electrical and Electronics Engineering, SR Engineering College, Warangal 506371, IndiaDepartment of Railroad and Electrical Engineering, Woosong University, Daejeon 34606, KoreaShort-term electric power load forecasting is a critical and essential task for utilities in the electric power industry for proper energy trading, which enables the independent system operator to operate the network without any technical and economical issues. From an electric power distribution system point of view, accurate load forecasting is essential for proper planning and operation. In order to build most robust machine learning model to forecast the load with a good accuracy irrespective of weather condition and type of day, features such as the season, temperature, humidity and day-status are incorporated into the data. In this paper, a machine learning model, namely a regression tree, is used to forecast the active power load an hour and one day ahead. Real-time active power load data to train and test the machine learning models are collected from a 33/11 kV substation located in Telangana State, India. Based on the simulation results, it is observed that the regression tree model is able to forecast the load with less error.https://www.mdpi.com/2073-431X/11/8/119load forecastingregression treehour-ahead marketday-ahead marketmachine learning |
| spellingShingle | Venkataramana Veeramsetty Modem Sai Pavan Kumar Surender Reddy Salkuti Platform-Independent Web Application for Short-Term Electric Power Load Forecasting on 33/11 kV Substation Using Regression Tree Computers load forecasting regression tree hour-ahead market day-ahead market machine learning |
| title | Platform-Independent Web Application for Short-Term Electric Power Load Forecasting on 33/11 kV Substation Using Regression Tree |
| title_full | Platform-Independent Web Application for Short-Term Electric Power Load Forecasting on 33/11 kV Substation Using Regression Tree |
| title_fullStr | Platform-Independent Web Application for Short-Term Electric Power Load Forecasting on 33/11 kV Substation Using Regression Tree |
| title_full_unstemmed | Platform-Independent Web Application for Short-Term Electric Power Load Forecasting on 33/11 kV Substation Using Regression Tree |
| title_short | Platform-Independent Web Application for Short-Term Electric Power Load Forecasting on 33/11 kV Substation Using Regression Tree |
| title_sort | platform independent web application for short term electric power load forecasting on 33 11 kv substation using regression tree |
| topic | load forecasting regression tree hour-ahead market day-ahead market machine learning |
| url | https://www.mdpi.com/2073-431X/11/8/119 |
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