Abstract
Electricity consumption forecasting has important implications for the mineral companies on guiding quarterly work, normal power system operation, and the management. However, electricity consumption prediction for the mineral company is difficult since electricity consumption can be affected by various factors. The problem is non-trivial due to three major challenges for traditional methods: insufficient training data, high computational cost and low prediction accuracy. To tackle these challenges, we firstly propose a Regressive Convolution Neural Network (RCNN) model, but RCNN still suffers from high computation overhead. Then we utilize RCNN to extract features from data and Regressive Support Vector Machine (SVR) trained with features to predict the electricity consumption. The experimental results show that RCNN-SVR model achieves higher accuracy than using the traditional RCNN or SVM alone. The MSE, MAPE, and CV-RMSE of RCNN-SVR model are 0.8564, 1.975, and 0.0687% respectively, which illustrates the low predicting error rate of the proposed model.
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Zhang, Y., Li, Q. (2020). A Regressive Convolution Neural Network and Support Vector Regression Model for Electricity Consumption Forecasting. In: Arai, K., Bhatia, R. (eds) Advances in Information and Communication. FICC 2019. Lecture Notes in Networks and Systems, vol 70. Springer, Cham. https://doi.org/10.1007/978-3-030-12385-7_4
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DOI: https://doi.org/10.1007/978-3-030-12385-7_4
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