Investigation for Applicability of Deep Learning Based Prediction Model in Energy Consumption Analysis

被引：0

作者：

Brijesh Singh ^{[1
]}

Jitendra Kumar Seth ^{[2
]}

Devansh Kumar Srivastava ^{[1
]}

Anchal Kumar Singh ^{[1
]}

Aman Mishra ^{[1
]}

机构：

[1] Department of EEE, KIET Group of Institutions, Ghaziabad

[2] Department of IT, KIET Group of Institutions, Ghaziabad

来源：

SN Computer Science | / 5卷 / 7期

关键词：

Deep learning; Gated recurrent unit; Load forecasting; LSTM; MAE; MSE; Recurrent neural network; RMSE;

D O I：

10.1007/s42979-024-03221-5

中图分类号：

学科分类号：

摘要：

The electricity demand is rising in our day-to-day life and business; therefore, it is important to forecast electricity consumption to balance the demand and supply chain. The prediction mechanism will enable us to meet the demand quickly and use the available resources optimally. It will help us to reduce the waste of energy. Several statistical methods have been proposed to solve this issue, but they do not achieve the goal due to tedious topology and monitoring mechanisms. This research aims to forecast the load hourly or annually without rigorous monitoring and with fewer resources using Deep Learning models. In this work, Recurrent Neural Network (RNN), Long-Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) based models are implemented. Their performances are measured using different evaluation metrics such as Mean Squared Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) to identify the best-performing model to forecast the load. The dataset is formed by collecting data from several open-source platforms containing the required parameters affecting the load day or night throughout all seasons. The proposed model can be utilized in the optimal production and supply of energy by accurately forecasting the energy load at a time. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

引用

共 23 条

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