Electricity demand forecasting algorithm development based on the gradient boosting machine by selecting the optimal combination of weather data and stations

초록

We predict electricity demand in a very high accuracy with various machine learning algorithms by designing optimal combinations of weather data, stations, and years. First, we create a novel forecasting structure by taking advantage of stair-wise structure of predicted weather data to increase the forecasting accuracy. Second, we test various machine learning algorithms: ridge regression, support vector machine, and gradient boosting machine (GBM). We find that the GBM has the best forecasting accuracy. Third, we select weather data, years of data, and weather stations based on root mean square errors. We also choose the weather stations by identifying a correlation between weather data and demand to compare two station-selecting methods. Accordingly, we increase the computation speed and forecasting accuracy by filtering out unrelated data. Finally, we verify our proposed algorithm by participating in the 2018 world electricity demand forecasting competition held in France. © The Korean Institute of Electrical Engineers.

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