Image-based with Peak Load Ensemble Prediction System for Demand Response in Smart Grid
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This paper proposes an image-based peak load prediction system for demand response in smart grids. Our aim is to enhance the prediction outcome especially during the season-changing days, also called shoulder-season. These days do not have a specific time throughout the year so we cannot use the season as a variable in the training set. We hypothesize that the approximate curve of the daily power consumption graph has some specific patterns that can be used to separate each day into different groups based on the pattern of the energy consumption curve. To this end, we use a convolution neural network model to classify and extract the features of the curve image. Then, we apply the k-means mechanism for image clustering to select better training sets and optimize the forecasting mechanism. Our results show an overall improvement of prediction during the season-changing period.