Energy Use Of Appliances Research Articles

Intelligent Energy Management: Leveraging an Effective Machine Learning for Predictive Appliance Energy Optimization in Smart Homes

In the context of escalating energy demands and the proliferation of smart home technologies, this study introduces a novel approach to energy management using the Random Forest machine learning model. Our research focuses on optimizing household appliance energy use, harmonizing efficiency with user comfort. By analyzing data on appliance usage patterns, environmental conditions, and user preferences, the Random Forest model predicts future energy needs, enabling the intelligent scheduling of appliances to reduce unnecessary consumption. The model’s strength lies in its capacity to unravel complex, non-linear relationships in high-dimensional data typical of household energy usage scenarios. Initial results demonstrate a notable decrease in energy consumption, affirming the model’s efficacy in enhancing energy efficiency without diminishing user convenience. This research not only highlights the potential of machine learning in energy management but also sets a foundation for future exploration into adaptive, real-time energy optimization strategies in smart homes. In this research, we compared our model with other machine learning models, and our model got a good accuracy of 95.71%, while for time series data, we got 99.71%.

Modelling Short-Term Appliance Energy Use with Interpretable Machine Learning: A System Identification Approach

The modelling and analysis of appliance energy use (AEU) of residential buildings are important for energy consumption control, energy management and maintenance, building performance evaluation, and so on. Although some traditional machine learning methods have been applied to produce good prediction results, these models are usually not interpretable, in that they fail to explain how appliance factors make contributions to the variation of AEU individually and interactively. Explicitly knowing the role played by each of the appliance factors in explaining AEU, however, is very important for energy saving. Motivated by this observation, this study introduces an interpretable machine learning approach which is built upon the nonlinear autoregressive moving average with eXogenous inputs model. The advantage of the proposed model is that in comparison with other state-of-the-art machine learning methods, for example, feedforward neural network, recurrent neural network (e.g., gated recurrent unit), and long short-term memory network, the established model is not only able to produce more accurate energy use prediction, but more importantly, also fully transparent and physically interpretable, clearly and explicitly indicating which factors significantly affect the variation of AEU. The findings of this study provide meaningful insights for improving the AEU efficiency.

How does information on environmental emissions influence appliance choice? The role of values and perceived environmental impacts

Energy labels with compact information on the energy use of appliances have been adopted worldwide to encourage consumers to make informed investment decisions. This study examines whether communicating environmental emissions (CO2 and air pollutant emissions) on energy labels can increase the preferences for energy-efficient appliances, with a specific focus on the role of individual psychological traits. A survey with decision tasks was conducted among the adult population in the Netherlands. Results show that information on environmental emissions does not increase the intention to invest in energy-efficiency, while energy-saving attitudes and habits do. Nevertheless, presenting information on CO2 emissions as well as combined CO2 and air pollutant emissions on energy labels can increase the preferences for energy-efficient appliances among individuals with relatively strong postmaterialist values. Information on CO2 emissions also increases the preferences of individuals with strong environmental concerns. However, no significant effects are found for information on air pollutant emissions. Also, personal experience of environmental degradation was not found to affect the effectiveness of the tested information interventions. Finally, we discuss the role of attention and type of decision situation for the processing of emissions information on energy labels. Our findings provide insights for improving information display on energy labels and energy education.

Forecasting of Appliances House in a Low-Energy Depend on Grey Wolf Optimizer

This paper gives and analyses data-driven prediction models for the energy usage of appliances. Data utilized include readings of temperature and humidity sensors from a wireless network. The building envelope is meant to minimize energy demand or the energy required to power the house independent of the appliance and mechanical system efficiency. Approximating a mapping function between the input variables and the continuous output variable is the work of regression. The paper discusses the forecasting framework FOPF (Feature Optimization Prediction Framework), which includes feature selection optimization: by removing non-predictive parameters to choose the best-selected feature hybrid optimization technique has been approached. k-nearest neighbors (KNN) Ensemble Prediction Models for the data of the energy use of appliances have been tested against some bases machine learning algorithms. The comparison study showed the powerful, best accuracy and lowest error of KNN with RMSE = 0.0078. Finally, the suggested ensemble model's performance is assessed using a one-way analysis of variance (ANOVA) test and the Wilcoxon Signed Rank Test. (Two-tailed P-value: 0.0001).

An Enhanced Ensemble Approach for Non-Intrusive Energy Use Monitoring Based on Multidimensional Heterogeneity.

Acting as a virtual sensor network for household appliance energy use monitoring, non-intrusive load monitoring is emerging as the technical basis for refined electricity analysis as well as home energy management. Aiming for robust and reliable monitoring, the ensemble approach has been expected in load disaggregation, but the obstacles of design difficulty and computational inefficiency still exist. To address this, an ensemble design integrated with multi-heterogeneity is proposed for non-intrusive energy use disaggregation in this paper. Firstly, the idea of utilizing a heterogeneous design is presented, and the corresponding ensemble framework for load disaggregation is established. Then, a sparse coding model is allocated for individual classifiers, and the combined classifier is diversified by introducing different distance and similarity measures without consideration of sparsity, forming mutually heterogeneous classifiers. Lastly, a multiple-evaluations-based decision process is fine-tuned following the interactions of multi-heterogeneous committees, and finally deployed as the decision maker. Through verifications on both a low-voltage network simulator and a field measurement dataset, the proposed approach is demonstrated to be effective in enhancing load disaggregation performance robustly. By appropriately introducing the heterogeneous design into the ensemble approach, load monitoring improvements are observed with reduced computational burden, which stimulates research enthusiasm in investigating valid ensemble strategies for practical non-intrusive load monitoring implementations.

Nontargeted vs. Targeted vs. Smart Load Shifting Using Heat Pump Water Heaters

Deployment of CTA-2045–enabled devices is increasing in the U.S. market. These devices allow utilities or third-party aggregators to control appliance energy use in homes, and could also be applied to end uses in small commercial buildings. This study focuses on a field study using CTA-2045–enabled water heaters to shift electric load off the peak and toward periods when renewable resources are more prevalent (e.g., near noon for solar resources and near midnight for wind resources). The following load shifting strategies were compared to understand effects on the aggregate load-shifting capabilities of Heat Pump Water Heaters (HPWHs) and on consumer hot water supply: non-targeted (traditional), targeted (grouped, with different shifting schedules) and “smart” (adaptive control commands). The results of this study show that targeted and smart control strategies yield significantly more load-shifting potential from a population of water heaters than the non-targeted approach without sacrificing hot water supply to occupants. However, as control commands become more aggressive, aggregators may face challenges in meeting consumer hot water demand. The findings and lessons learned can benefit electric utilities and inform updates to manufacturer controls and communications standards. The data collected may also be useful for developing and validating HPWH models.

Performance gap in a multi-storey student accommodation complex built to Passivhaus standard

Although the Passivhaus design is rapidly spreading worldwide, limited studies have examined its actual performance during operation. This study used Australia's first student accommodation built to the Passivhaus standard as a case study to assess the gaps between design prediction and in situ performance. Passivhaus criteria, supplemented with the adaptive thermal comfort approach, were applied to evaluate the discrepancies between design predictions and measurements. This year-long study discovered significant gaps in three indicators, including primary energy renewable, overheating frequency, and space heating energy demand. The simulation was over-optimistic in the first two indicators but conservative in the last. The discrepancies were mainly caused by the default assumptions and inherent restrictions of the simulation platform. Improvements can be achieved by incorporating more advanced datasets for the estimation of appliance energy use, improving the feedback process from measurements to the simulation tool and developing a more integrated simulation package. The results of this study contribute toward a deeper understanding of the magnitude, causes, and solutions of performance gaps between design and operation stages for large Passivhaus projects.

Office Building Tenants’ Electricity Use Model for Building Performance Simulations

Large office buildings are responsible for a substantial portion of energy consumption in urban districts. However, thorough assessments regarding the Nordic countries are still lacking. In this paper we analyse the largest dataset to date for a Nordic office building, by considering a case study located in Stockholm, Sweden, that is occupied by nearly a thousand employees. Distinguishing the lighting and occupants’ appliances energy use from heating and cooling, we can estimate the impact of occupancy without any schedule data. A standard frequentist analysis is compared with Bayesian inference, and the according regression formulas are listed in tables that are easy to implement into building performance simulations (BPS). Monthly as well as seasonal correlations are addressed, showing the critical importance of occupancy. A simple method, grounded on the power drain measurements aimed at generating boundary conditions for the BPS, is also introduced; it shows how, for this type of data and number of occupants, no more complexities are needed in order to obtain reliable predictions. For an average year, we overestimate the measured cumulative consumption by only 4.7%. The model can be easily generalised to a variety of datasets.

Dishing on dishwashing for a greener planet and playing games in a glove box

A dish-ertation Consider the dishwasher paradox. Dishwashers save time and water compared with hand washing dishes while accounting for only about 10% of an average household’s appliance energy use. Yet these well-meaning machines cause nearly half of all appliance-based how-to-use disputes, according to the observations of this Newscripts writer. Discussions about whether to prerinse—that is, to rinse dishes before loading—can be as contentious as some political debates. And disagreements over how to load dishes can send passive-aggressive chills through an otherwise happy home. Thankfully, science can help resolve this problem. Last month, researchers led by sustainability expert Gregory A. Keoleian of the University of Michigan shared some science-based best practices for the dishwashing arts. Collaborating with the appliance maker Whirlpool, the researchers considered the materials and energy costs of making a dishwasher, washing a decade’s worth of dishes, and ultimately disposing of a machine that had washed its last (Environ.

Energy use prediction with information theory and machine learning technique

Appliances energy consumption plays an increasingly important role in the overall building electric energy consumption and its temporal trending. However, predicting appliances energy consumption is complicated by lack of causal understanding of the appliances energy use as well as too many potential predictors that might be relevant to the appliances energy use. In this study, we apply information theory and advance machine learning neural network technique to first rank the importance of potential drivers that dominate appliances energy consumption and secondly model the temporal evolution of appliances energy consumption with a restricted set of environmental predictors. Our results showed that temperature and humidity were the two most important environmental drivers in the house appliances energy consumption modeling. Furthermore, using those environmental drivers, the machine learning model was able to accurately capture the temporal dynamics of appliances energy consumption.

Data compression approach for the home energy management system

As a typical energy-cyber-physical system (e-CPS), home energy management system (HEMS) plays a critical role in power systems by accommodating higher levels of renewable generation, reducing power costs, and decreasing consumer energy bills. HEMS can help understand the home appliances energy use and learn the users’ preference so as to optimize home appliances operation and achieve higher energy efficiency. HEMS needs massive historical and real-time data for the above applications. Since HEMS is always based on a wireless sensor network, a more effective online data compression approach is necessary. The efficient data compression methods can not only relieve data transmission pressure and reduce data storage overhead, but also enhance data analysis efficiency. This paper proposes an online pattern-based data compression approach for the data generated by home appliances. The proposed approach first discovers the patterns of the time series data and then utilizes these patterns for the online data compression. The pattern discovery method in the proposed approach includes an online adaptive segmenting algorithm with incremental processing technique and a similarity metric based on piecewise statistic distance. The key issues of parameter selection and data reconstruction are also presented. Real-world common home appliance datasets are employed for comparing the performance of the proposed approach with those of six state-of-the-art algorithms. The experimental results demonstrate the outperformance of the proposed approach. Further complexity analysis shows that the proposed approach has linear time complexity. To the best of our knowledge, this is the first paper that performs online data compression based on the extracted patterns of the time series.

Domestic Energy Consumption Modeling per Physical Characteristics and Behavioral Factors

This manuscript introduced a domestic energy consumption modeling method which incorporated physical characteristics and behavioral factors. The model was established by considering regular load modeling and prediction characteristics as well as user behavior. A typical home energy hub serves as the research object; physical and operating features were input to model appliance energy use. We then simulated user energy consumption behaviors by combining non-intrusive load decomposition and Markov Chain methods based on the proposed model. Practical examples were used to determine the model’s prediction and the effects of demand response. The proposed model showed an independent ability to depict the load portrait curve with relatively little input data.

Predicting unusual energy consumption events from smart home sensor network by data stream mining with misclassified recall

With the popularity and affordability of ZigBee wireless sensor technology, IoT-based smart controlling system for home appliances becomes prevalent for smart home applications. From the data analytics point of view, one important objective from analyzing such IoT data is to gain insights from the energy consumption patterns, thereby trying to fine-tune the energy efficiency of the appliance usage. The data analytics usually functions at the back-end crunching over a large archive of big data accumulated over time for learning the overall pattern from the sensor data feeds. The other objective of the analytics, which may often be more crucial, is to predict and identify whether an abnormal consumption event is about to happen. For example, a sudden draw of energy that leads to hot spot in the power grid in a city, or black-out at home. This dynamic prediction is usually done at the operational level, with moving data stream, by data stream mining methods . In this paper, an improved version of very fast decision tree (VFDT) is proposed, which learns from misclassified results for the sake of filtering the noisy data from learning and maintaining sharp classification accuracy of the induced prediction model. Specifically, a new technique called misclassified recall (MR), which is a pre-processing step for self-rectifying misclassified instances, is formulated. In energy data prediction, most misclassified instances are due to data transmission errors or faulty devices. The former case happens intermittently, and the errors from the latter cause may persist for a long time. By caching up the data at the MR pre-processor, the one-pass online model learning can be effectively shielded in case of intermitting problems at the wireless sensor network; likewise the stored data could be investigated afterwards should the problem persist for long. Simulation experiments over a dataset about predicting exceptional appliances energy use in a low energy building are conducted. The reported results validate the efficacy of the new methodology VFDT + MR, in comparison to a collection of popular data stream mining algorithms from the literature.

Preferred attributes of home energy management products for smart grids - results of a design study and related user survey

This paper presents an insight into end-users' perception of smart grid products for households. The analysed products included three types of home energy management products (HEMPs) namely: smart thermostats, smart plugs and smart wall sockets. The analysis involved existing commercial HEMPs, as well as newly designed HEMPs from a students' project executed at University of Twente (Netherlands) in 2013 and 2014. Various industrial design methods were applied, and an online survey was utilised for data collection. The smart thermostat was considered the product with the greatest potential to stimulate energy-efficient behaviour. Features most preferred by end-users are: 1) visual display of energy information; 2) monitoring of energy use of appliances; 3) remote control, and expected ease of use. Appearance also appeared to have influenced the preferences of end-users regarding specific HEMPs. This study highlights the main features that household end-users desire in products that could stimulate energy-efficient behaviour.

A preliminary investigation of water usage behavior in single-family homes

As regional drought conditions continue deteriorating around the world, residential water use has been brought into the built environment spotlight. Nevertheless, the understanding of water use behavior in residential buildings is still limited. This paper presents data analytics and results from monitoring data of daily water use (DWU) in 50 single-family homes in Texas, USA. The results show the typical frequency distribution curve of the DWU per household and indicate personal income, education level and energy use of appliances all have statistically significant effects on the DWU per capita. Analysis of the water-intensive use demonstrates the residents tend to use more water in post-vacation days. These results help generate awareness of water use behavior in homes. Ultimately, this research could support policy makers to establish a water use baseline and inform water conservation programs.

Data driven prediction models of energy use of appliances in a low-energy house

This paper presents and discusses data-driven predictive models for the energy use of appliances. Data used include measurements of temperature and humidity sensors from a wireless network, weather from a nearby airport station and recorded energy use of lighting fixtures. The paper discusses data filtering to remove non-predictive parameters and feature ranking. Four statistical models were trained with repeated cross validation and evaluated in a testing set: (a) multiple linear regression, (b) support vector machine with radial kernel, (c) random forest and (d) gradient boosting machines (GBM). The best model (GBM) was able to explain 97% of the variance (R2) in the training set and with 57% in the testing set when using all the predictors. From the wireless network, the data from the kitchen, laundry and living room were ranked the highest in importance for the energy prediction. The prediction models with only the weather data, selected the atmospheric pressure (which is correlated to wind speed) as the most relevant weather data variable in the prediction. Therefore, atmospheric pressure may be important to include in energy prediction models and for building performance modeling.

Impact of global warming on thermal performance of domestic buildings using probabilistic climate data

The uncertainty in climate change predictions is an important factor in research into the impact of global warming on the thermal environment of buildings. This paper explores the impact of climate change on a domestic building in the UK, and assesses the measures that can be taken for better adaptation to such changes. Probabilistic climate change projections (UKCP09) are used to explore the uncertainties in predictions of annual heating energy, carbon emissions, and overheating risk due to climate change for a representative case study house. Global sensitivity analysis methods are implemented to identify key variables affecting these three performance indicators. Simulations indicate that the uncertainties in overheating risk will become significantly larger over time than uncertainties in heating energy and carbon emissions. For this case study building, the room heating set-point temperature has more influence on heating energy use compared to continuous/intermittent heating mode. The energy use of appliances is the most influential source for carbon emissions in this house.

Impact of global warming on thermal performance of domestic buildings using probabilistic climate data

The uncertainty in climate change predictions is an important factor in research into the impact of global warming on the thermal environment of buildings. This paper explores the impact of climate change on a domestic building in the UK, and assesses the measures that can be taken for better adaptation to such changes. Probabilistic climate change projections (UKCP09) are used to explore the uncertainties in predictions of annual heating energy, carbon emissions, and overheating risk due to climate change for a representative case study house. Global sensitivity analysis methods are implemented to identify key variables affecting these three performance indicators. Simulations indicate that the uncertainties in overheating risk will become significantly larger over time than uncertainties in heating energy and carbon emissions. For this case study building, the room heating set-point temperature has more influence on heating energy use compared to continuous/intermittent heating mode. The energy use of appliances is the most influential source for carbon emissions in this house.

A Stackelberg game-theoretic approach to optimal real-time pricing for the smart grid

This paper proposes a Stackelberg game approach to maximize the profit of the electricity retailer (utility company) and minimize the payment bills of its customers. The electricity retailer determines the retail price through the proposed smart energy pricing scheme to optimally adjust the real-time pricing with the aim to maximize its profit. The price information is sent to the customers through a smart meter. According to the announced price, the customers can automatically manage the energy use of appliances in the households by the proposed optimal electricity consumption scheduling system with the aim to minimize their electricity bills. We model the interactions between the retailer and its electricity customers as a 1-leader, N-follower Stackelberg game. At the leader's side, i.e., for the retailer, we adopt genetic algorithms to maximize its profit while at the followers' side, i.e., for customers, we develop an analytical solution to the linear programming problem to minimize their bills. Simulation results show that the proposed approach is beneficial for both the customers and the retailer.

Creating an in-home display: Experimental evidence and guidelines for design

In-home electricity displays (IHDs) are digital devices that can give near-real-time information about electricity usage in the home. These devices have the potential to provide the kind of personalized feedback necessary to effect behavioral change among residential consumers. However, for consumers to be able to act on the information provided on IHDs, they must first be able to understand it. We present an approach to in-home display design that uses research on customer preferences to determine which features to experimentally examine for customer comprehension. Additionally, we compare these preferences against experimental data to determine whether people have insight into what information best works for an increased understanding of energy saving. Using a computer-based simulated IHD, we find that the types of feedback information that consumers prefer (appliance-specific and dollar-feedback) are not as effective for learning about appliance energy use as the less-preferred aggregated kWh feedback. Moreover, it appears that a simpler more generalized format of information provision has the potential to be more effective than a personalized IHD. We discuss how consumer preferences and experimental tests can jointly be used to inform the design of feedback technologies.