In the era of Digital Transformation, every application runs on power which is nothing but electricity. So, a proper mechanism is required to analyze the capacity of electricity used in our day-to-day life for domestic as well as industrial purposes. Prediction of price of electricity not only provides you information about how much you need to pay but also provide information about how much capacity you are using how much is required and related information. As a proper analysis and prediction of electricity is required, this research work has developed a model to predict electricity by using machine learning algorithms. Electricity price prediction depends on different factors like national wind, wind production and natural factors, etc., due to which it is a challenging task. Our application deals with regression analysis to forecast the value of the dependent variable. The proposed dataset consists of 18 fields like date and time, month, year, holidays. This study has used four types of regressors namely Random Forest Regression, Logistic Regression, Support Vector Regressor, and Artificial Neural Network Regressor to predict the price. This study has considered the Mean Absolute Error (MAE) as an evaluation metric and compared all the models. The best result is obtained by the ANN Regressor algorithm.
₹10000 (INR)
IEEE-2023