Development of a Machine Learning Algorithm for Predicting Electrical Consumption

In the contemporary era, the pursuit of precise predictions based on datasets is predominantly motivated by the science of computer systems. Therefore, this study attempts to further enhance prediction efforts. A city-wide electricity consumption dataset, which includes temporal and environmental characteristics, is analyzed. This dataset is subjected to rigorous preprocessing to extract relevant characteristics. A wide range of machine learning models, such as XGBoost and Python’s Scikit, are used to build prediction models. These models are then rigorously trained and tuned using sophisticated optimization techniques for optimal performance. Finally, the evaluation of their efficiency, interpretability, and computational efficiency is derived. Furthermore, programming techniques are investigated, and new structures and learning methods elucidate intricate patterns and relationships in the data. This approach facilitates a more comprehensive understanding of the pivotal factors influencing electrical consumption trends and enables the identification of the most suitable methodologies for specific prediction tasks. In conclusion, the present study contributes by utilizing the knowledge gained from previous research for the advancement of predictive analysis. The findings have the potential to be useful in decision-making processes, optimizing resource allocation, and improving urban planning and management practices.