The short-term residential electricity demand forecast tasks are essential for designing the portfolio of the decision-making process of aggregators for the selection of control targets and derivation of specific control target values for demand response. The implementation of edge computing on the forecast functionality, which provides forecasting and minimal communication functions based on the data accumulated at the end-user-side using an inexpensive computational resource deployed locally, is an attractive framework for practical implementation. To achieve good accuracy while forecasting the demands of individual households during practical long-term operation, it is necessary to follow the changes in demand characteristics quickly by updating the prediction models frequently. This study focused on the concept of deep reservoir computing, which has a high affinity for implementation in edge computing frameworks from the viewpoint of hardware implementability, while utilizing the deep architecture and avoiding high computational cost. In particular, this study proposed an online learning scheme for the implementation of edge computing, which does not require large-scale computation for updating the prediction models. The effectiveness of the proposed approach was evaluated considering the calculation cost and accuracy through numerical experiments using real-world residential load data.
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