A Comprehensive Energy Management Application Method considering Smart Home Occupant Behavior using IoT and Real Big Data

TL;DR

This paper presents an IoT-based energy management system for smart homes that uses big data and predictive modeling to optimize energy use, reduce costs, and improve efficiency.

Contribution

It introduces a decentralized management approach combining forecasting, optimization, and scheduling using real big data and MILP, tailored for smart homes with PV and EV.

Findings

Forecasting models achieved high accuracy with 4 years of data.

Energy costs reduced by up to 62.05%.

Peak-to-average ratio decreased by up to 44.19%.

Abstract

One of the most far-reaching use cases of the internet of things is in smart grid and smart home operation. The smart home concept allows residents to control, monitor, and manage their energy consumption with minimum loss and self-involvement. Since each household's lifestyle and energy consumption is unique, the management system needs background knowledge about residents' energy consumption behavioral patterns for more accurate planning. To obtain this information, data related to residents' consumption records must be processed. This research has attempted to provide an optimal decentralized management system consisting of interoperable sections to forecast, optimize, schedule, and implement load management in a smart home. Comparing different prediction models using 4 years of 1-min interval real data of a smart home with photovoltaic generation (PV) and electric vehicle (EV), forecasting non-controllable loads and taking a deterministic approach in different scenarios, the system uses mixed integer linear programming (MILP) to provide load scheduling with the objective of an optimal total energy cost reduction with minimum changes in the household's desired consumption compared to the initial state. The results have shown that the proposed system has reliable performance due to the high precision of the forecast and has led to increased energy efficiency, reduced energy cost (up to 62. 05\%), reduced peak-to-average ratio (PAR) (up to 44. 19\%) and reduced standard deviation (SD) (up to 19. 70\%) in net consumption.