Résumé:
This thesis focuses on developing an accurate electricity consumption forecasting model
within smart grids by leveraging modern artificial intelligence techniques to enhance energy
management and improve efficiency in residential environments. The study began with the
preparation and preprocessing of detailed electricity consumption data for a single household,
including measurements such as active power, reactive power, voltage, current intensity, and
consumption from sub-metered appliances.
Initially, a Long Short-Term Memory (LSTM) neural network model was applied for load
forecasting. However, after comparative evaluation, the XGBoost model demonstrated superior
accuracy and performance in reducing prediction errors. Consequently, efforts were
concentrated on optimizing the XGBoost model through hyperparameter tuning and measure
the features (using normalization or standardization), resulting in highly accurate forecasts with
minimal error margins.
To extend the scope of the research, the model was applied to six different households in a
specific area, with hourly and daily predictions performed to provide a detailed and real-time
view of electricity consumption patterns. This expansion allowed for a comprehensive
understanding of consumption variability and enhanced the model’s predictive capability.
In the final phase, an integrated energy management framework was developed based on the
forecasting results. This framework optimizes the utilization of multiple energy sources
including solar, wind, battery storage, and the electrical grid thus enabling efficient energy
distribution and reducing reliance on the grid when possible. The framework represents a
significant step towards building intelligent and sustainable energy systems capable of adapting
to demand fluctuations and maximizing operational efficiency.
This thesis makes a valuable scientific and practical contribution to the field of smart grids
by combining AI techniques with energy management strategies. It opens new avenues for
innovative solutions that support energy sustainability and help reduce environmental impacts
in the future.
