| Title |
Analysis of the Effect of Weather Factors for Short-Term Load Forecasting |
| Authors |
권보성(Bo-Sung Kwon) ; 박래준(Rae-Jun Park) ; 송경빈(Kyung-Bin Song) |
| DOI |
https://doi.org/10.5370/KIEE.2020.69.7.985 |
| Keywords |
Load Forecasting; Behind-The-Meter Generation; Deep Neural Networks |
| Abstract |
With the spread of renewable energy, the accuracy of load forecasting has been getting worse. The main cause of error in load forecasting is that the effect of behind-the-meter(BTM) generation is not well considered. In order to improve the accuracy of short-term load forecasting(STLF), the effects of weather factors on load forecasting should be systematically analyzed. A load forecasting model based on deep neural networks is used for analysis. There are several weather factors which are temperature, humidity, wind speed, solar radiation, cloud cover, precipitation, and precipitation probability, etc. Main purpose of the study is finding a combination of weather factors that have a good effect on improving STLF. The load forecast is performed from 2016 to 2018 to analyze forecast errors by using various weather factor combinations in case studies. The test results show that the case of using temperature, solar radiation, and precipitation as input data for weather is the most accurate among the nine case studies in STLF. The mean absolute percentage error(MAPE) at that case is 1.46% for the case studies from 2016 to 2018 |