Blockchain and Deep Learning for Smart Healthcare

14Medical Appliances Energy Consumption Prediction Using Various Machine Learning Algorithms

Kaustubh Pagar¹, Tarun Jain¹, Horesh Kumar², Aditya Bhardwaj^3* and Rohit Handa⁴

¹Manipal University Jaipur, Dehmi Kalan, India ²Greater Noida Institute of Technology, Greater Noida, India

³School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India

⁴Data Analytics at Lead Technology, Synechron, Toronto, Canada

Abstract

One of the greatest inventions of the 19th century was electricity, which now has become an important part of our day-to-day life. However, many sources of electricity are exhaustible, and their production and distribution are costly, which makes it necessary to use this invention wisely and judiciously. The main focus of this chapter is the prediction of energy use of electrical appliances that are usually found in a normal household. An IOT-based wireless sensor is used to track the weather conditions of the rooms of the house that are used to estimate the electrical intake of the household devices. It is also significantly impacted by the weather conditions. In this chapter, we attempt to create a learning model using various regression analysis models like Linear Regression, Random Forest, Support Vector Regressor (SVR), K-Nearest Regressor, ExtraTree Regressor, and so on. The ExtraTree Regressor gives the best result among all models, with an R² score of 0.65.

Keywords: Regression, energy consumption, prediction, appliances, machine ...