smart grid short-term load forecasting framework using advanced machine learning (ML), deep learning (DL), and statistical modeling techniques
Budget: £250 – £750 GBP
updates:
This project focuses on developing a smart grid short-term load forecasting framework using advanced machine learning (ML), deep learning (DL), and statistical modeling techniques to improve energy demand prediction and optimize grid performance. With the increasing complexity of modern power systems, accurate load forecasting plays a vital role in ensuring grid stability, reducing operational costs, and integrating renewable energy sources.
The project begins with acquiring and analyzing historical energy consumption datasets from sources such as National Grid ESO and smart meter data repositories. Initial work includes data preprocessing, handling missing values, feature engineering, and identifying correlations between energy demand and influencing factors like time, weather, and seasonal variations.
Baseline models such as ARIMA, SARIMA, and Holt-Winters are developed to establish reference performance. Building upon these, advanced machine learning models like Random Forest, Gradient Boosting, and XGBoost are implemented to improve accuracy. Finally, deep learning architectures, including LSTM and GRU networks, are used to capture complex temporal dependencies for robust forecasting.
The project compares all models based on RMSE, MAE, and MAPE metrics and selects the best-performing approach. The chosen model is then integrated into MATLAB/Simulink or Python-based simulations to evaluate its impact on smart grid energy management strategies such as peak load balancing, demand response, and renewable integration.
This research contributes to designing an optimized, data-driven energy forecasting framework suitable for UK smart grids. The outcome will guide utility providers and policymakers in achieving efficient energy management and preparing for future challenges in sustainable power systems.
This project focuses on developing a smart grid short-term load forecasting framework using advanced machine learning (ML), deep learning (DL), and statistical modeling techniques to improve energy demand prediction and optimize grid performance. With the increasing complexity of modern power systems, accurate load forecasting plays a vital role in ensuring grid stability, reducing operational costs, and integrating renewable energy sources.
The project begins with acquiring and analyzing historical energy consumption datasets from sources such as National Grid ESO and smart meter data repositories. Initial work includes data preprocessing, handling missing values, feature engineering, and identifying correlations between energy demand and influencing factors like time, weather, and seasonal variations.
Baseline models such as ARIMA, SARIMA, and Holt-Winters are developed to establish reference performance. Building upon these, advanced machine learning models like Random Forest, Gradient Boosting, and XGBoost are implemented to improve accuracy. Finally, deep learning architectures, including LSTM and GRU networks, are used to capture complex temporal dependencies for robust forecasting.
The project compares all models based on RMSE, MAE, and MAPE metrics and selects the best-performing approach. The chosen model is then integrated into MATLAB/Simulink or Python-based simulations to evaluate its impact on smart grid energy management strategies such as peak load balancing, demand response, and renewable integration.
This research contributes to designing an optimized, data-driven energy forecasting framework suitable for UK smart grids. The outcome will guide utility providers and policymakers in achieving efficient energy management and preparing for future challenges in sustainable power systems.