Adaptive System for Human Scenario Forecasting in Intelligent Homes
P. Kalyani, K. V. Prashanth, R. Manivannan, Raksha Puthran, Shruti Jalapur, Dhananjaya Babu K · 2024
Due to the rise in energy usage in recent years, there has been a worldwide push to reduce energy use, particularly in the housing sector. When the concept of a "smart house" first emerged, it brought together automation and control via communication network technologies, as well as warmth from many heat source mechanisms and the terminology "process controllers" stated earlier. The Internet of Things, or IoT, is a multibillion dollar industry that shows how overlapping technologies span industries. It does this by utilizing the internet as an objective standard for sharing information that IoT services manage, supervise, or locate.Utilizing home automation systems, big data, and machine learning, together with the Internet of Things, to increase energy efficiency is another exciting topic. This article introduces HEMS-IoT, a comprehensive energy management system designed for the modern home. It leverages big data and machine learning to provide security and convenience in a comprehensive, eco-efficient setting. Weka API and the J48 neural network technology were used to extract user behavior and energy consumption trends. This approach was used to categorize loads depending on households.Apache Mahout with Rule-based language (RuleML) in a smart house for user comfort and security This is because these technologies are able to predict what consumers would require for energy action and supply it during this consultation because to the aggregation of user preferences. XtraPrinting As they demonstrated how they would monitor the instance, the article offers an experimental way for monitoring intelligent buildings for comfort and safety with the least amount of energy use. Therefore, their approach, which explains the demonstration success rate, is responsible for the test’s high performance.