International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 06 | Jun 2025
p-ISSN: 2395-0072
www.irjet.net
Prediction and Optimization of the fuel cost of a power system using Machine Learning Approaches Mr. P. Bhanu prakash1, Dr. P.Pardhasaradhi2, Mr. Karimulla Polisetti3 , Mrs. P. Rupa4 1M.Tech Student, Department of CSE, Bapatla Engineering College, Bapatla. 2Professor, Department of CSE, Bapatla Engineering College, Bapatla.
3Assistant professor, Department of EEE, Bapatla Engineering College, Bapatla. 4Assistant professor, Department of ECE, Bapatla Engineering College, Bapatla.
---------------------------------------------------------------------***--------------------------------------------------------------------compare to conventional and bees algorithm by using Abstract - : Day by day power demand increasing rapidly
various generator inputs [1].The ∑- constraint method is used in this three generator inputs used, Lambda iteration method used [2].Newton –Rap son method has proved to be fast and accurate solving the Constraint equations [4].It presents a new emission dispatch solution algorithm that achieving minimum cost by curtailing that generating units are high then emissions and cost also high [5]. The Improved Tabu Search Algorithm is used to solve the Economic Dispatch problem for 3-generator test system, 6- generator test system and 13-generator test system. The simulation is carried out using C++ software. The total operating cost and emission level of system is minimized [6]. An optimization technique has been presented for the economical location of real and reactive power applying the linear programming method and an algorithm has been developed for finding a post emergency schedule with the minimum of load shedding [8].In this paper section-II describes multi linear regression method. Section-III describes stochastic gradient decent approach for optimizing the fuel cost. In section-IV describes Results and discussion, system parameters and input data are present.
and fuel cost also increases in power generating stations. To overcome this problem we are using machine learning models for optimizing the fuel cost .This paper presents predicting the fuel cost based on data using multi linear regression machine learning approach. The machine learning model is working perfectly and accurately. To optimize the fuel cost of a power system stochastic gradient descent model is used. This proposed procedure proves the multi-objective optimization problem. On comparing with Bees and conventional algorithm Bat is the efficient for optimization
Key Words:
Multi linear regression, Stochastic gradient descent model, Multi-objective optimization problem.
1. INTRODUCTION In present scenario power demand increasing day by day. We are using so many electrical appliances it consumes more power. The power comes from power generating stations. In thermal power plants it generates electricity by burning fossil fuels. The fuel cost also increasing day by day. The power generating industries facing economical problems due to fuel cost increases. To reducing the fuel cost using multi-objective optimization techniques. In this paper we are using machine learning approach for optimization compare to previous optimization techniques the machine learning stochastic gradient descent method is very efficient. Machine learning is used for improving the model accuracy to adjust the parameters of a system to achieve the best performance. Machine learning model allows models to train data in a reasonable time and avoid unnecessary computations. In many real time applications understanding the models and predictions are very important. The machine learning models is easier to understand the system behavior. The stochastic gradient descent method is used to optimize and enables models and offering the real time predictive and optimization capabilities.
2. MULTI LINEAR REGRESSION Multi linear regression is a method to understand the multiple independent variables and dependent variables. It helps to identifying the most wanted factor for predicting the outputs. The factors like all system parameters, when the model is created, it can be used for prediction, this makes multi linear regression is a powerful tool for forecasting and decision-making. It allows multiple dependent variables simultaneously.
This paper describes several multi objective optimization techniques used in this bat algorithm working perfectly
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