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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, wedoi:10.3390/su12166364 fatcat:v5ih63jo2jckrbfucmbv5nmgi4