  Integrating 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, enabling more efficient management of renewable energy
resources. We use long short-term memory networks, well-suited for time series
data, to capture complex patterns and dependencies in energy demand data. The
proposed approach is evaluated using four historical short-term energy demand
data datasets 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: Facebook
Prophet, Support Vector Regression, and Random Forest Regression. 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 has the potential to
manage the integration of renewable energy sources in an effective manner.
