A Regressive Convolution Neural network and Support Vector Regression Model for Electricity Consumption Forecasting

TL;DR

This paper introduces a hybrid RCNN-SVR model for electricity consumption forecasting in mineral companies, effectively addressing data scarcity, computational costs, and accuracy issues.

Contribution

The paper proposes a novel hybrid RCNN and SVR approach that improves prediction accuracy and reduces errors compared to traditional methods.

Findings

RCNN-SVR outperforms RNN and SVM in accuracy

Achieves low MSE, MAPE, and CV-RMSE values

Effective for mineral company electricity consumption prediction

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 different from traditional electricity load prediction since mineral company electricity consumption can be affected by various factors (e.g., ore grade, processing quantity of the crude ore, ball milling fill rate). The problem is non-trivial due to three major challenges for traditional methods: insufficient training data, high computational cost and low prediction accu-racy. To tackle these challenges, we firstly propose a Regressive Convolution Neural Network (RCNN) to predict the electricity consumption. While RCNN still suffers from high computation overhead, we utilize RCNN to extract features from the history data and Regressive Support Vector Machine (SVR) trained with the features to predict the electricity consumption. The experimental results show that the proposed RCNN-SVR model achieves higher accuracy than using the traditional RNN 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.