Electricity consumption forecasting in smart homes with LSTM networks

The work aims to enhance the efficiency of electricity consumption management in smart homes through a forecasting model based on a Long Short-Term Memory (LSTM) neural network, which significantly improves prediction accuracy by capturing long-term dependencies in time series data.A formalized representation of the system's structure and mathematical relations has been provided to support comprehensive data analysis and parameter tuning.An advanced forecasting algorithm has been designed, incorporating historical electricity usage and external factors such as weather conditions and time of day.A dedicated software solution has been implemented using LSTM-based deep learning architecture, combined with data normalization and encoding of temporal features.Additionally, a data acquisition and processing subsystem has been integrated with smart energy meters to facilitate real-time analytics.The model was validated using both real-world and synthetic datasets, confirming its high forecasting performance.A systematic development approach ensured component modularity, scalability, and robust data protection.The system was developed using Python, TensorFlow, and Keras, with deployment capabilities via modern API tools, ensuring adaptability to dynamic environments and seamless integration with other smart infrastructure.Evaluation results demonstrate that the trained model delivers accurate forecasts with minimal deviation from actual consumption values.Functional testing confirmed the correctness of computations and algorithmic stability.Future research directions include refining hyperparameter optimization methods for the LSTM model, expanding the set of input variables, and integrating the system with automated energy management platforms.These steps will contribute to more efficient energy use, cost reduction, and enhanced reliability of electricity supply under unstable grid conditions.