Improving Forecasting Efficiency Using Machine Learning and IoT - Gaidhane, Manisha; Mulchandani, Mona; Dudhe, Priyanka (International Journal of Computer Sciences and Engineering, 2025)
Forecasting can be defined as prediction of what is going to happen in the future by analyzing the past and current available data. It can be done for power, weather, business, company management, economics, investors etc. Although it varies based on the area for which it is going to be applied. Forecasting has technical and business impacts. If it is not done properly, it can cause inefficient usage of resources. In traditional load forecasting, predicting future demands is a quite time consuming and sometimes it results in the incorrect output. To overcome these challenges, new generation technologies should be utilized such as internet of things, cloud computing, and machine learning. It can also help in improving existing established systems. The purpose of this study paper is to know, participation of new technologies in improving the efficiency of forecasting. Forecasting using machine-learning and IoT would really help to achieve high forecast accuracy. In machine learning forecasting, processors learn from mining loads of cloud data without human intervention to fulfill the demand. While doing this paper, by the manner of literature review, first, the trend of improvement, diversification and the new characteristics of the system will be evaluated. Then, the forecasting technology will be reviewed and analyzed from two different aspects, elementary analysis and application research. This review paper study will help to create a new system idea that would provide more accurate forecasting with reduction in time consumption.