A comprehensive evaluation of machine learning algorithms for precise energy consumption forecasting in smart homes

Aug 3, 2026

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Energy consumption forecasting in smart homes supports energy sustainability and cost stability as demand continues rising with technological progress. This work proposes an evaluation-driven prediction model that performs data preprocessing, applies multiple machine-learning algorithms to forecast consumption, and compares their effectiveness using standard performance metrics such as MAE, MSE, and R-squared. The study addresses the practical challenge of selecting the most suitable algorithm as environments and datasets vary.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages7

Reading Time18 min

AccessFree to download

Introduction

Smart grids and energy management

Energy consumption prediction with machine learning

Related work and motivation

A comprehensive evaluation of machine learning algorithms for precise energy consumption forecasting in smart homes Lakshmana Phaneendra Maguluri1, M. Shankar2, R. Aruna3, D. Chitra Devi4, M. J. Suganya5 1Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, India 2Department of Computer Science & Technology, Madanapalle Institute of Technology & Science, Madanapalle, India 3Department of Electronics and Communication Engineering, AMC Engineering College, Bengaluru, India 4Department of Computer Science and Engineering, S.A. Engineering College, Chennai, India 5Department of Electrical and Electronics Engineering, Panimalar Engineering College, Chennai, India Article history: Received Feb 7, 2024 Revised Aug 31, 2024 Accepted Sep 6, 2024 Keywords: Load forecasting Machine learning algorithms Performance metrics Smart grid Smart home Corresponding Author: Energy is one of the most critical and costly resources, playing a vital role in our daily lives. As technology advances, the demand for energy also increases. This work proposes a model for predicting energy consumption in smart homes, consisting of data preprocessing, performance evaluation, and application. Once the data is processed, it is fed into the prediction module, where various machine-learning algorithms are applied to forecast energy consumption. As smart home environments grow in complexity, selecting the most effective machine learning algorithm becomes increasingly crucial. The persistent challenge lies in manually discerning the best-performing algorithm, given their potential variance in efficacy across diverse use cases or datasets. In the dynamic landscape of energy conservation and costeffective power generation, precise forecasting of energy consumption is essential, playing a pivotal role in advancing energy sustainability and bolstering economic stability. This introduction explores the intricate terrain of predicting energy utilization within smart homes, a domain that has seen increased interest due to the integration of machine learning algorithms. The primary focus of this exploration is the rigorous evaluation of these algorithms, using key performance metrics such as mean absolute error (MAE), mean squared error (MSE), and R-squared. This is an open access article under the CC BY-SA license. M. J. Suganya Department of Electrical and Electronics Engineering, Panimalar Engineering College Varadharajapuram, Poonamallee, Chennai 600123, India Email: [sugi.mj@gmail.com](sugi.mj@gmail.com) Article Info ABSTRACT 1. INTRODUCTION Smartgrids (SG) have emerged as a viable solution to meet the growing global energy demand. The term "grid" refers to the traditional electrical infrastructure comprising transmission lines, substations, and other elements that facilitate the delivery of energy from power plants to homes and businesses [1], [2] . What sets Smart Grids apart is their capability for two-way communication between utility providers and consumers, coupled with sensing capabilities along the grid lines. Key components of a smart grid include controls, computers, automation systems, and other advanced technologies working in tandem to address the rapid surge in energy requirements shown in Figure 1. The intelligence embedded in smart grids brings forth various benefits. Noteworthy advantages include more efficient energy transmission, enhanced security measures, and the ability to mitigate peak demand, consequently leading to a reduction in electricity rates. Smart grids are also recognized for their integration of renewable energy sources, aligning with the global push towards sustainable and eco-friendly power generation. Overall, smart grids represent a significant advancement in the energy sector, ensuring a more responsive and adaptable infrastructure to meet the evolving needs of our energyintensive world [3]-[5] . Efficient energy management systems (EMS) relies he

FAQs

What is the main goal of the study on smart homes?

To predict energy consumption in smart homes using machine learning models and to rigorously evaluate which algorithms perform best.

Which evaluation metrics are used for algorithm comparison?

The study uses mean absolute error (MAE), mean squared error (MSE), and R-squared.

Why is selecting an effective machine learning algorithm challenging in this context?

Because algorithm performance can vary across different use cases and datasets, requiring careful evaluation rather than manual selection.

Aug 3, 2026

Upload by Skyler