The electric energy consumption prediction (EECP) is an essential and complex task in\nintelligent power management system. EECP plays a significant role in drawing up a national energy\ndevelopment policy. Therefore, this study proposes an Electric Energy Consumption Prediction\nmodel utilizing the combination of Convolutional Neural Network (CNN) and Bi-directional Long\nShort-Term Memory (Bi-LSTM) that is named EECP-CBL model to predict electric energy consumption.\nIn this framework, two CNNs in the first module extract the important information from several\nvariables in the individual household electric power consumption (IHEPC) dataset. Then, Bi-LSTM\nmodule with two Bi-LSTM layers uses the above information as well as the trends of time series in\ntwo directions including the forward and backward states to make predictions. The obtained values\nin the Bi-LSTM module will be passed to the last module that consists of two fully connected layers\nfor finally predicting the electric energy consumption in the future. The experiments were conducted\nto compare the prediction performances of the proposed model and the state-of-the-art models for the\nIHEPC dataset with several variants. The experimental results indicate that EECP-CBL framework\noutperforms the state-of-the-art approaches in terms of several performance metrics for electric energy\nconsumption prediction on several variations of IHEPC dataset in real-time, short-term, medium-term\nand long-term time spans.
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