| dc.description.abstract | This project presents a fuel demand prediction system designed to forecast the amount of fuel required by a specific branch of a fuel station. The increasing complexity of fuel consumption patterns and the need for efficient inventory management in the fuel industry motivated the development of this predictive model.
The objectives of this project were to analyze historical fuel consumption data, identify influential factors affecting fuel demand, and develop an accurate prediction model. The system utilizes machine learning algorithms and time series analysis techniques to forecast fuel requirements based on factors such as historical consumption patterns and days of the week.
The methodology involved collecting a large dataset of fuel consumption records from the target branch over a nine-month period. Exploratory data analysis techniques were applied to identify trends and patterns. The dataset was then preprocessed, including data cleaning, feature engineering, and normalization.
Neural networks were used for this project The models were trained on a subset of the data and validated to ensure robustness.
The impact of this fuel demand prediction system is significant for fuel station management, enabling more efficient inventory planning, reducing costs associated with excess or insufficient fuel supply, and optimizing customer service. Additionally, the system offers valuable insights for branch managers to make informed decisions regarding staffing, marketing promotions, and resource allocation.
In conclusion, this project successfully developed a fuel demand prediction system that leverages machine learning techniques to forecast the amount of fuel required by a specific fuel station branch. The accurate predictions obtained through this system contribute to improved inventory management, cost savings, and enhanced operational efficiency within the fuel industry. | en_US |
