(untitled) (International Journal of Innovative Research in Engineering & Management, 2023)
This Paper proposes a data-driven approach for grid loss prediction in power systems. It utilizes a comprehensive dataset with relevant features such as grid load, temperature forecasts, and calendar data. The dataset is pre-processed by handling missing values, normalizing features, and encoding cyclic calendar features. A Long Short-Term Memory (LSTM) recurrent neural network is employed for the prediction model, capturing temporal dependencies and generating forecasts of grid loss two hours ahead. The model is trained using mean absolute error (MAE) as the loss function and optimized through hyperparameter tuning. Evaluation metrics like MAE and root mean squared error (RMSE) assess the model’s accuracy. Visualization techniques compare predicted and actual grid loss values. The Paper concludes with analysis, future research suggestions, and highlights the potential of the Prophet data-driven approach for efficient and reliable power distribution.