Comparison of Recurrent Neural Network Model for Future Electric Power Prediction | IEEE Conference Publication | IEEE Xplore

Comparison of Recurrent Neural Network Model for Future Electric Power Prediction


Abstract:

The increasing demand for electricity can be directly attributed to the exponential rise in both the global population and the sophistication of modern technologies. Beca...Show More

Abstract:

The increasing demand for electricity can be directly attributed to the exponential rise in both the global population and the sophistication of modern technologies. Because electric power is utilized simultaneously with its generation at the power station, it is crucial to precisely anticipate electric power consumption in advance to ensure a reliable power supply. Estimating future electricity needs is critical to the success of any electricity provider. Having accurate predictions of electricity use has several practical and financial benefits, including ensuring the security and stability of the power grid. This research employs a Recurrent Neural Network (RNN) model based on a Long Short-Term Memory (LSTM) network to anticipate electricity usage, aiming to resolve the discrepancy between the need for precise prediction and the limitations of conventional methods. The model incorporates conventional LSTM and hybrid Convolutional Neural Networks and Long Short-Term Memory (CNN-LSTM). First, we’ll examine the Kaggle data we collected on electricity consumption. Second, we utilize an RNN model to forecast future power consumption as a whole. Finally, we will utilize loss and Mean Square Error (MSE) to qualitatively and statistically assess the performance of the model. According to the research conducted in the experiments, the CNN-LSTM prediction model outperforms traditional LSTM models in terms of reliable prediction.
Date of Conference: 10-12 November 2022
Date Added to IEEE Xplore: 22 December 2022
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Conference Location: Dharan, Nepal

I. Introduction

The amount of energy consumed is an important factor to consider while discussing energy infrastructure. Following the energy crisis of the 1970s [1], people began to consider their energy consumption. It has also highlighted how global energy use is increasing at an alarming rate. As a result, governments attempt to cut energy use in a variety of areas, including building, agriculture, industry, transportation, and others [2]. Because of the variety of energy sources, tracking the consumption of fossil fuels, renewable resources, and nuclear power is a time-consuming and labour-intensive operation. However, by doing so, we can anticipate how much energy will be needed in different locations and work on establishing plans that are specific to those regions and their needs.

References

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