An Improved Model for Short Term load forecasting and Price Forecasting using Novel Machine learning for various areas of Lahore Pakistan

Abstract

The economy relies on accurate projections of power usage. Accurate predictions of power consumption are essential for supply-side policymakers. In contrast, when there is a lack of data and considerations, it might be difficult to make accurate predictions. Consequently, using the original data sequence, we developed the DCOGM forecasting model, which combines data transformation with background value interpolation optimization (1,1). To test the accuracy of DCOGM (1,1) in simulation and prediction, two case studies are conducted. Most existing enhanced grey models do not do as well in forecasting as DCOGM (1,1) does, according to the data DCOGM is used to estimate Pakistan's Lahore City's overall energy consumption from 2017 to 2021. (1,1). According to the DCOGM (1,1) model, Lahore's demand for energy will increase over the next four years, comparing to those other grey adjustment models and the regular GM (1,1). It is possible to employ DCOGM (1,1) as a short-term forecasting approach in addition to other forecasting challenges with a limited number of data points.