Short-Term Electricity Demand Forecasting: Impact Analysis of Temperature for Thailand
Accurate electricity demand forecasting for a short horizon is very important for day-to-day control, scheduling, operation, planning, and stability of the power system. The main factors that affect the forecasting accuracy are deterministic variables and weather variables such as types of days and...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-05-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/13/10/2498 |
| _version_ | 1797567864307187712 |
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| author | Kamal Chapagain Somsak Kittipiyakul Pisut Kulthanavit |
| author_facet | Kamal Chapagain Somsak Kittipiyakul Pisut Kulthanavit |
| author_sort | Kamal Chapagain |
| collection | DOAJ |
| description | Accurate electricity demand forecasting for a short horizon is very important for day-to-day control, scheduling, operation, planning, and stability of the power system. The main factors that affect the forecasting accuracy are deterministic variables and weather variables such as types of days and temperature. Due to the tropical climate of Thailand, the marginal impact of weather variables on electricity demand is worth analyzing. Therefore, this paper primarily focuses on the impact of temperature and other deterministic variables on Thai electricity demand. Accuracy improvement is also considered during model design. Based on the characteristics of demand, the overall dataset is divided into four different subgroups and models are developed for each subgroup. The regression models are estimated using Ordinary Least Square (OLS) methods for uncorrelated errors, and General Least Square (GLS) methods for correlated errors, respectively. While Feed Forward Artificial Neural Network (FF-ANN) as a simple Deep Neural Network (DNN) is estimated to compare the accuracy with regression methods, several experiments conducted for determination of training length, selection of variables, and the number of neurons show some major findings. The first finding is that regression methods can have better forecasting accuracy than FF-ANN for Thailand’s dataset. Unlike much existing literature, the temperature effect on Thai electricity demand is very interesting because of their linear relationship. The marginal impacts of temperature on electricity demand are also maximal at night hours. The maximum impact of temperature during night hours happens at 11 p.m., is 300 MW/<inline-formula> <math display="inline"> <semantics> <msup> <mrow></mrow> <mo>°</mo> </msup> </semantics> </math> </inline-formula>C, about <inline-formula> <math display="inline"> <semantics> <mrow> <mn>4</mn> <mo>%</mo> </mrow> </semantics> </math> </inline-formula> rise in demand while during day hours, the temperature impact is only 10 MW/<inline-formula> <math display="inline"> <semantics> <msup> <mrow></mrow> <mo>°</mo> </msup> </semantics> </math> </inline-formula>C to 200 MW/<inline-formula> <math display="inline"> <semantics> <msup> <mrow></mrow> <mo>°</mo> </msup> </semantics> </math> </inline-formula>C about <inline-formula> <math display="inline"> <semantics> <mrow> <mn>1.4</mn> <mo>%</mo> </mrow> </semantics> </math> </inline-formula> to <inline-formula> <math display="inline"> <semantics> <mrow> <mn>2.6</mn> <mo>%</mo> </mrow> </semantics> </math> </inline-formula> rise. |
| first_indexed | 2024-03-10T19:48:26Z |
| format | Article |
| id | doaj.art-336c8a722ebe462cb42ad3242f2c8166 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T19:48:26Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-336c8a722ebe462cb42ad3242f2c81662023-11-20T00:35:12ZengMDPI AGEnergies1996-10732020-05-011310249810.3390/en13102498Short-Term Electricity Demand Forecasting: Impact Analysis of Temperature for ThailandKamal Chapagain0Somsak Kittipiyakul1Pisut Kulthanavit2Sirindhorn International Institute of Technology, Thammasat University, PathumThani 12000, ThailandSirindhorn International Institute of Technology, Thammasat University, PathumThani 12000, ThailandFaculty of Economics, Thammasat University, Bangkok 10200, ThailandAccurate electricity demand forecasting for a short horizon is very important for day-to-day control, scheduling, operation, planning, and stability of the power system. The main factors that affect the forecasting accuracy are deterministic variables and weather variables such as types of days and temperature. Due to the tropical climate of Thailand, the marginal impact of weather variables on electricity demand is worth analyzing. Therefore, this paper primarily focuses on the impact of temperature and other deterministic variables on Thai electricity demand. Accuracy improvement is also considered during model design. Based on the characteristics of demand, the overall dataset is divided into four different subgroups and models are developed for each subgroup. The regression models are estimated using Ordinary Least Square (OLS) methods for uncorrelated errors, and General Least Square (GLS) methods for correlated errors, respectively. While Feed Forward Artificial Neural Network (FF-ANN) as a simple Deep Neural Network (DNN) is estimated to compare the accuracy with regression methods, several experiments conducted for determination of training length, selection of variables, and the number of neurons show some major findings. The first finding is that regression methods can have better forecasting accuracy than FF-ANN for Thailand’s dataset. Unlike much existing literature, the temperature effect on Thai electricity demand is very interesting because of their linear relationship. The marginal impacts of temperature on electricity demand are also maximal at night hours. The maximum impact of temperature during night hours happens at 11 p.m., is 300 MW/<inline-formula> <math display="inline"> <semantics> <msup> <mrow></mrow> <mo>°</mo> </msup> </semantics> </math> </inline-formula>C, about <inline-formula> <math display="inline"> <semantics> <mrow> <mn>4</mn> <mo>%</mo> </mrow> </semantics> </math> </inline-formula> rise in demand while during day hours, the temperature impact is only 10 MW/<inline-formula> <math display="inline"> <semantics> <msup> <mrow></mrow> <mo>°</mo> </msup> </semantics> </math> </inline-formula>C to 200 MW/<inline-formula> <math display="inline"> <semantics> <msup> <mrow></mrow> <mo>°</mo> </msup> </semantics> </math> </inline-formula>C about <inline-formula> <math display="inline"> <semantics> <mrow> <mn>1.4</mn> <mo>%</mo> </mrow> </semantics> </math> </inline-formula> to <inline-formula> <math display="inline"> <semantics> <mrow> <mn>2.6</mn> <mo>%</mo> </mrow> </semantics> </math> </inline-formula> rise.https://www.mdpi.com/1996-1073/13/10/2498short-term electricity demand forecastingThai electricity demandtemperature impact on electricity demandfeed-forward neural networkmultiple linear regression |
| spellingShingle | Kamal Chapagain Somsak Kittipiyakul Pisut Kulthanavit Short-Term Electricity Demand Forecasting: Impact Analysis of Temperature for Thailand Energies short-term electricity demand forecasting Thai electricity demand temperature impact on electricity demand feed-forward neural network multiple linear regression |
| title | Short-Term Electricity Demand Forecasting: Impact Analysis of Temperature for Thailand |
| title_full | Short-Term Electricity Demand Forecasting: Impact Analysis of Temperature for Thailand |
| title_fullStr | Short-Term Electricity Demand Forecasting: Impact Analysis of Temperature for Thailand |
| title_full_unstemmed | Short-Term Electricity Demand Forecasting: Impact Analysis of Temperature for Thailand |
| title_short | Short-Term Electricity Demand Forecasting: Impact Analysis of Temperature for Thailand |
| title_sort | short term electricity demand forecasting impact analysis of temperature for thailand |
| topic | short-term electricity demand forecasting Thai electricity demand temperature impact on electricity demand feed-forward neural network multiple linear regression |
| url | https://www.mdpi.com/1996-1073/13/10/2498 |
| work_keys_str_mv | AT kamalchapagain shorttermelectricitydemandforecastingimpactanalysisoftemperatureforthailand AT somsakkittipiyakul shorttermelectricitydemandforecastingimpactanalysisoftemperatureforthailand AT pisutkulthanavit shorttermelectricitydemandforecastingimpactanalysisoftemperatureforthailand |