A Carbon-Aware IoT Scheduling Framework for Sustainable Smart Homes Using Predictive Energy Models

Abstract

With the widespread popularization of software engineering-based Internet of Things technology, the smart home field has obtained the basic conditions to realize fine-grained monitoring and intelligent control of household energy consumption. Presently, there are two big issues with our home energy management. First, in this paper, our emphasis is only on conserving energy rather than considering whether the energy we consume from the grid is clean or dirty. Second, we are not making sure that the software we use to schedule our energy use is reliable. To address these issues, this study proposes a carbon-aware home energy management framework that integrates predictive modeling, environmentally conscious scheduling, and software reliability validation. The three major entities of the proposed scheme are predicting the closeness, predicting energy usage, and scheduling to utilize energy to reduce our carbon footprint. In this context, utilizing a special formula that considers both energy consumption and carbon emissions, we can find ways to make our homes better and more efficient for the planet. For this newly proposed system, all simulation experiments were completed on the OMNeT++ platform. After the simulation tests were finished, several types of reliability verification tests were also conducted. The results of these tests are clear: all core performance indicators of this new framework outperform those of the benchmark method used as a reference. This system can provide an accurate, long-term usable solution for next-generation smart home energy management systems.

Received: 6 May 2026 | Revised: 1 July 2026 | Accepted: 9 September 2026

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

The data that support the findings of this study are openly available in the UCI Machine Learning Repository at https://archive.ics.uci.edu/ml/datasets/individual+household+elect ric+power+consumption. The grid carbon-intensity data are openly available from the National Grid Carbon Intensity API at https://carbonintensity.org.uk/. The OMNeT++ simulation configurations, preprocessing scripts, and source code used in this study are available from the corresponding author upon reasonable request.

Author Contribution Statement

Ali Raad Abdulkareem: Conceptualization, Methodology, Investigation, Writing – original draft, Project administration. Mustafa Moosa Qasim: Conceptualization, Methodology, Software, Formal analysis, Data curation, Writing – original draft, Visualization. Husham Lateef Swadi Roomi: Software, Investigation, Data curation, Visualization. Murtadha Al-Maliki: Investigation, Resources. Mahmood A. Al-Shareeda: Conceptualization, Methodology, Validation, Formal analysis, Writing – review & editing, Supervision, Project administration. Mohammed Amin Almaiah: Validation, Formal analysis, Resources, Writing – review & editing, Supervision, Funding acquisition. Rami Shehab: Validation, Resources, Writing – review & editing, Funding acquisition.