Predicting Short Term Energy Demand in Smart Grid: A Deep Learning Approach for Integrating Renewable Energy Sources in Line with SDGs 7, 9, and 13

The integration of renewable energy sources into the power grid is becoming increasingly important as the world moves towards a more sustainable energy future in line with SDG 7. However, the intermittent nature of renewable energy sources can make it challenging to manage the power grid and ensure a stable supply of electricity, which is crucial for achieving SDG 9. In this paper, we propose a deep learning-based approach for predicting energy demand in a smart power grid, which can improve the integration of renewable energy sources by providing accurate predictions of energy demand. Our approach aligns with SDG 13 on climate action as it enables more efficient management of renewable energy resources. We use long short-term memory networks, which are well-suited for time series data, to capture complex patterns and dependencies in energy demand data. The proposed approach is evaluated using four datasets of historical short term energy demand data from different energy distribution companies including American Electric Power, Commonwealth Edison, Dayton Power and Light, and Pennsylvania-New Jersey-Maryland Interconnection. The proposed model is also compared with three other state of the art forecasting algorithms namely, Facebook Prophet, Support Vector Regressor, and Random Forest Regressor. The experimental results show that the proposed REDf model can accurately predict energy demand with a mean absolute error of 1.4%, indicating its potential to enhance the stability and efficiency of the power grid and contribute to achieving SDGs 7, 9, and 13. The proposed model also have the potential to manage the integration of renewable energy sources in an effective manner.

REDf: A Renewable Energy Demand Forecasting Model for Smart Grids using Long Short Term Memory Network

The integration of renewable energy sources into the power grid is becoming increasingly important as the world moves towards a more sustainable energy future. However, the intermittent nature of renewable energy sources can make it challenging to manage the power grid and ensure a stable supply of electricity. In this paper, we propose a deep learning-based approach for predicting energy demand in a smart power grid, which can improve the integration of renewable energy sources by providing accurate predictions of energy demand. We use long short-term memory networks, which are well-suited for time series data, to capture complex patterns and dependencies in energy demand data. The proposed approach is evaluated using four datasets of historical energy demand data from different energy distribution companies including American Electric Power, Commonwealth Edison, Dayton Power and Light, and Pennsylvania-New Jersey-Maryland Interconnection. The proposed model is also compared with two other state of the art forecasting algorithms namely, Facebook Prophet and Support Vector Regressor. The experimental results show that the proposed REDf model can accurately predict energy demand with a mean absolute error of 1.4%. This approach has the potential to improve the efficiency and stability of the power grid by allowing for better management of the integration of renewable energy sources.