International Journal of Science, Engineering and TechnologyAuthors: Paruchuri Raghava Rani, Associate Professor Ch.Naveen
Abstract: Institutions that distribute power and companies that produce electricity, whether public or commercial, rely heavily on long-term electricity demand estimates. It helps with strategic decision-making to improve the quality of energy output and ensures optimal energy utilisation. Countries that have endured energy shortages for a long time, like Iraq, have a pressing need for this. Based on daily home power usage statistics collected from the Iraqi Ministry of power for the Rusafa district of Baghdad between 2022 and 2024, this research draws its conclusions. We also added meteorological data from the same years, which includes things like humidity, sun radiation, and temperature, all of which are outside influences on consumption habits. This paper presents a hybrid model for forecasting that combines LSTM and CNN-based deep learning architectures, an improved stacked hybrid model that uses CNN, GRU, Stacked Bi-LSTM, and machine learning regressors like XGBoost and LightGBM, and so on. The goal of training these models is to enhance energy acoustic production techniques and provide more accurate forecasts. Using metrics like mean relative absolute error (MAPE) and mean root mean square error (RMSE), we trained and assessed the proposed model across 30 epochs to assess the accuracy of the predictions. Our hybrid model utilising the LightGBM regressor outperformed all others examined, with a MAPE of 0.185155 and an RMSE of 0.094603 for the next spilt time period of periodic predictions, respectively. The findings highlight the promise of hybrid modelling approaches for improving power distribution system optimisation and energy forecasting.
