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Short-Dataset-Driven Prediction on Area Electricity Consumption with Adaptive Training Window Selection

Accurate electricity consumption predictions are expected to play an important role in the future new energy internet system, i.e., guiding the dynamic capacity increasing of power grid, and in balancing the loads of power supply lines, and predicting risks of power outage. However, the electricity consumption of an area varies along with time, i.e., the electricity consumption datasets are strictly time series. Note that traditional time series forecasting algorithms prefer having longer dataset and their performance with relatively shorter datasets are usually not guaranteed. Targeting at the scenario with only short datatsets, we in this work propose to applying one-dimensional convolution neural network enhanced by a designed adaptive sliding window selection. The proposed algorithm dynamically adjusts the size of sliding window according to a set of factors, i.e., temperature, weather, the length of training data and so on, via applying the Light Gradient Boosting Machine. By means of simulations, we show the effectiveness of the proposed short-dataset-driven prediction for electricity consumption forecasting tasks.