Elsevier

Internet of Things

Volume 20, November 2022, 100636
Internet of Things

Research article
A machine-learning ensemble model for predicting energy consumption in smart homes

https://doi.org/10.1016/j.iot.2022.100636Get rights and content

Abstract

Smart homes incorporate several devices that automate tasks and make our lives easy. These devices can be useful for many things, like security access, lighting, temperature, etc. Using the Internet of Things (IoT) platform, smart homes essentially let homeowners control appliances and devices remotely. Due to their self-learning skills, smart homes can learn homeowners' schedules and adapt accordingly to make adjustments. Since convenience and cost savings is necessary in such an environment, and there are multiple devices involved, there is a need to analyze power consumption in smart homes. Moreover, increased energy consumption leads to an increase in carbon footprint, elevates the risk of climate, and leads to increased demand in supply. Hence, monitoring energy consumption is crucial. In this paper, we perform an overall analysis of energy consumption in smart homes by deploying machine learning models. We rely on machine learning techniques, like Decision Trees (DT), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and k-Nearest Neighbor (KNN) for predicting the power consumption of multiple datasets. We also propose a DT-RF-XGBoost-based Ensemble Model for analyzing the consumption and comparing it with the baseline algorithms. The evaluation parameters used in the study are Mean Square Error (MSE), R-squared (R2,), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE), respectively. The study has been performed on multiple datasets and our study shows that the proposed DT-RF-XG-based Ensemble Model outperforms all the other baseline algorithms for multiple datasets with R2 around 0.99.

Introduction

One of the best applications of Artificial Intelligence (AI) in modern times is in the form of smart homes. Smart homes are residences that rely on internet-connected devices for remote monitoring and device and appliance management [1]. It is also referred to as home automation or domotics and is concerned with providing comfort, security, energy efficiency, and convenience. Smart devices are usually controlled by a smart home application or a networked device. Smart homes are built on the Internet of Things (IoT) platform and incorporate sensors, speakers, smart bulbs, cameras, locks, door openers, etc. [2]. Due to their self-learning skills, they are capable of learning the homeowners’ schedules and can adjust accordingly. Additionally, these devices may operate together and share consumer usage data due to automation based on homeowners' preferences [3]. Since these devices are power-driven, they can reduce power consumption and lead to energy-related cost savings [4].

While smart home devices can save a considerable amount of energy, the efficiency can be improvised even further. Many smart speakers and connected cameras consume more power since they add more energy load. Since power consumption relies on many predictable factors like what devices were used before, what devices are being used presently, which product is bought, and how it is used, a hundred percent power saving cannot be guaranteed. The methods to address the issue are being actively researched.

Another major reason that leads to inefficient power consumption is based on poorly constructed buildings. For constructions with a single-pane window with no insulation, deploying a smart thermometer may not be beneficial. In other words, if the building is not designed to save energy, integrating applications and components may be a tedious task. This may lead to greater power consumption. Moreover, as more and more smart homes house intelligent lighting controls, although it may use very little electricity, the fact that it is smart and always connected may lead to more consumption of electricity in general [5]. Hence, there is a need to inspect power consumption for smart homes.

Monitoring power consumption is also necessary for load-balancing power plants. Performing load study is an important aspect of energy monitoring. As there is an increase in energy consumption, it adds to the risk of climate change and increases carbon footprints [6]. The increasing energy costs, in turn, lead to an increase in the demand for energy consumption. Owing to all these factors, there is a need to monitor energy consumption. One of the ways of inspecting power consumption is by taking a look at the prediction data. To reduce power consumption in smart homes, it is necessary to observe prediction trends. In the past several methods have been proposed to monitor power consumption. Some of these methods are in-chip configurations in microprocessing systems, digital power meters, energy auditing tools, delay and power monitoring schemes [7], etc. Moreover, machine learning methods have been applied in abundance to monitor power consumption. Linear Regression [8], Support Vector Machines [9], and Long-short Term Memory [10,11], etc., are some of the popular machine-learning models that have been relied on in the past for analyzing power consumption. While traditional machine learning models yield satisfactory results for addressing the problem, the models often have limitations. Moreover, overfitting and cost are prevalent in most traditional machine learning algorithms. To address limitations like these, ensemble methods are deployed. Ensemble methods combine many machine learning algorithms to produce one optimal predictive model, thereby enhancing the model's performance and robustness.

The novelty and main contributions of the paper are as follows:

  • 1

    We propose an ensemble-based technique for predicting power consumption in smart homes. The ensemble proposed is a combination of Decision Trees (DT), Random Forests (RF), and eXtreme Gradient Boosting (XGBoost).

  • 2

    We compare the performance of our proposed ensemble method with several other baseline models such as K Nearest Neighbors (KNN), Decision Trees (DT), Random Forests (RF), and Gradient Boosting (GB). Deploying the ensemble model has two advantages. First, it improves the prediction performance over the other contributing components of the ensemble. Second, it reduces the variance of prediction errors induced by components of the ensemble, thereby addressing any kind of overfitting.

  • 3

    The performance of the models is evaluated using multiple statistical parameters such as Mean Square Error (MSE), R-squared Error (R2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE).

  • 4

    Extensive analysis has been performed on two different datasets that incorporate readings with a time span of 1 minute of house applications in kiloWatts from smart meters. To the best of our knowledge, this is the first paper highlighting power consumption in smart homes using the DT-RF-GB-based ensemble approach.

The rest of the paper has been organized as follows. Section 2 presents some relevant related works in the area. Section 3 denotes the machine learning algorithms deployed for the ensemble method proposed. In Section 4, we detail the experimental analysis, including the datasets and evaluation parameters. This section also includes the results obtained from the extensive study, along with a comparative analysis. Section 5 discusses conclusions and future works.

Section snippets

Related works

In this section, we present a detailed survey of past related works. Since energy management is a global issue, the area has witnessed extensive research over the last few decades. The progress in technology has led to several methods being proposed for energy monitoring as well as management. We highlight some proposed methods in this section and propose an ensemble-based approach in the next section.

Ref. [12] proposed a Multi-output Adaptive neuro-fuzzy inference system (MANFIS) based smart

Machine learning methods and our proposed ensemble-based method

In this paper, we use machine learning models extensively, including DT, RF, and XGBoost. In addition, we proposed a hybrid method based on a Decision Tree, Random Forest, and eXtreme Gradient Boost, called the DT-RF-XGBoost Ensemble method, which gives much better performance compared to the individual machine learning method.

Experimentation results

In this section, we highlight the datasets followed by the evaluation metrics. Based on the experimental analysis, we also present the experimental results in scatter plots, pair plots, tables, and bar graphs.

Conclusions and future work

Increased energy consumption has led to an increased carbon footprint and elevated climate change risk. Due to the higher demand for energy across the globe, not only higher costs of energy are incurred, but also there is a constant demand for supply. Hence monitoring energy consumption is necessary to manage energy costs and realize saving opportunities. One of the common ways of monitoring energy consumption is by predicting its usage.

In this study, we have deployed four machine learning

Declaration of Competing Interest

None.

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