A big data framework for short-term power load forecasting using heterogenous data
The power system is in a transition towards a more intelligent, flexible and interactive system with higher penetration of renewable energy generation, load forecasting, especially short-term load forecasting for individual electric customers plays an increasingly essential role in future grid plann...
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2022-12-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/thesisDetails#10.11959/j.issn.1000-0801.2022292 |
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| _version_ | 1850212587807965184 |
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| author | Haibo ZHAO Zhijun XIANG Linsong XIAO |
| author_facet | Haibo ZHAO Zhijun XIANG Linsong XIAO |
| author_sort | Haibo ZHAO |
| collection | DOAJ |
| description | The power system is in a transition towards a more intelligent, flexible and interactive system with higher penetration of renewable energy generation, load forecasting, especially short-term load forecasting for individual electric customers plays an increasingly essential role in future grid planning and operation.A big data framework for short-term power load forcasting using heterogenous was proposed, which collected the data from smart meters and weather forecast, pre-processed and loaded it into a NoSQL database that was capable to store and further processing large volumes of heterogeneous data.Then, a long short-term memory (LSTM) recurrent neural network was designed and implemented to determine the load profiles and forecast the electricity consumption for the residential community for the next 24 hours.The proposed framework was tested with a publicly available smart meter dataset of a residential community, of which LSTM’s performance was compared with two benchmark algorithms in terms of root mean square error and mean absolute percentage error, and its validity has been verified. |
| format | Article |
| id | doaj-art-01ab92d5f4dd4c7dad32825d05bbf0a5 |
| institution | OA Journals |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2022-12-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-01ab92d5f4dd4c7dad32825d05bbf0a52025-08-20T02:09:18ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012022-12-013810311159574658A big data framework for short-term power load forecasting using heterogenous dataHaibo ZHAOZhijun XIANGLinsong XIAOThe power system is in a transition towards a more intelligent, flexible and interactive system with higher penetration of renewable energy generation, load forecasting, especially short-term load forecasting for individual electric customers plays an increasingly essential role in future grid planning and operation.A big data framework for short-term power load forcasting using heterogenous was proposed, which collected the data from smart meters and weather forecast, pre-processed and loaded it into a NoSQL database that was capable to store and further processing large volumes of heterogeneous data.Then, a long short-term memory (LSTM) recurrent neural network was designed and implemented to determine the load profiles and forecast the electricity consumption for the residential community for the next 24 hours.The proposed framework was tested with a publicly available smart meter dataset of a residential community, of which LSTM’s performance was compared with two benchmark algorithms in terms of root mean square error and mean absolute percentage error, and its validity has been verified.http://www.telecomsci.com/thesisDetails#10.11959/j.issn.1000-0801.2022292short-term load forecasting;long short-term memory network;recurrent neural network;clustering;big data |
| spellingShingle | Haibo ZHAO Zhijun XIANG Linsong XIAO A big data framework for short-term power load forecasting using heterogenous data Dianxin kexue short-term load forecasting;long short-term memory network;recurrent neural network;clustering;big data |
| title | A big data framework for short-term power load forecasting using heterogenous data |
| title_full | A big data framework for short-term power load forecasting using heterogenous data |
| title_fullStr | A big data framework for short-term power load forecasting using heterogenous data |
| title_full_unstemmed | A big data framework for short-term power load forecasting using heterogenous data |
| title_short | A big data framework for short-term power load forecasting using heterogenous data |
| title_sort | big data framework for short term power load forecasting using heterogenous data |
| topic | short-term load forecasting;long short-term memory network;recurrent neural network;clustering;big data |
| url | http://www.telecomsci.com/thesisDetails#10.11959/j.issn.1000-0801.2022292 |
| work_keys_str_mv | AT haibozhao abigdataframeworkforshorttermpowerloadforecastingusingheterogenousdata AT zhijunxiang abigdataframeworkforshorttermpowerloadforecastingusingheterogenousdata AT linsongxiao abigdataframeworkforshorttermpowerloadforecastingusingheterogenousdata AT haibozhao bigdataframeworkforshorttermpowerloadforecastingusingheterogenousdata AT zhijunxiang bigdataframeworkforshorttermpowerloadforecastingusingheterogenousdata AT linsongxiao bigdataframeworkforshorttermpowerloadforecastingusingheterogenousdata |