Abstract
Monthly electric load forecasting is essential to efficiently operate urban power grids. Although diverse forecasting models based on artificial intelligence techniques have been proposed with good performance, they require sufficient datasets for training. In the case of monthly forecasting, because just one data point is generated per month, it is not easy to collect sufficient data to construct models. This lack of data can be alleviated using transfer learning techniques. In this paper, we propose a novel monthly electric load forecasting scheme for a city or district based on transfer learning using similar data from other cities or districts. To do this, we collected the monthly electric load data from 25 districts in Seoul for five categories and various external data, such as calendar, population, and weather data. Then, based on the available data of the target city or district, we selected similar data from the collected datasets by calculating the Pearson correlation coefficient and constructed a forecasting model using the selected data. Lastly, we fine-tuned the model using the target data. To demonstrate the effectiveness of our model, we conducted an extensive comparison with other popular machine-learning techniques through various experiments. We report some of the results.
Highlights
With the recent increase in the use of fossil fuels to cope with the explosive demand for energy, diverse global problems, such as greenhouse gas and energy resource depletion, have attracted much attention
We considered the Pearson correlation coefficients (PCCs) to determine the similarity between the two domains
We concatenated them to develop a training set for pre-training the deep neural network (DNN) model
Summary
With the recent increase in the use of fossil fuels to cope with the explosive demand for energy, diverse global problems, such as greenhouse gas and energy resource depletion, have attracted much attention. Mid-term load forecasting is especially critical to enforcing a reliable power system in a smart city by making it possible to generate electricity depending on the future energy demand [24]. Seoul, which is the capital and largest metropolis in South Korea, has been pursuing a leading smart city, which requires an accurate monthly electric load forecasting model for annual urban energy planning [25,26]. We propose a novel transfer learning-based monthly load forecasting model for cities or districts using other domain data that have a high correlation coefficient with the target data. We use the transfer learning technique to perform accurate hierarchical monthly electric load forecasting for metropolitan cities using public datasets.
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