Energy-saving Recommendations Research Articles

SMART HOME ENERGY SAVING WITH BIG DATA AND MACHINE LEARNING

In response to escalating energy consumption, particularly within the housing sector, a global imperative to reduce energy usage has emerged, propelling the concept of "smart houses" to the forefront of innovation. This paradigm shift owes its genesis to the convergence of advancements in energy conversion, communication networks, and information technology, catalyzing the emergence of the Internet of Things (IoT). The IoT facilitates seamless connectivity of devices via the World Wide Web, enabling remote management, monitoring, and detection capabilities. Capitalizing on this technological synergy, the integration of IoT, big data, and machine learning with home automation systems holds immense promise for enhancing energy efficiency. This paper introduces HEMS-IoT, a groundbreaking energy control system for intelligent homes, underpinned by big data analytics and machine learning algorithms, prioritizing security, convenience, and energy conservation. Leveraging J48 neural network technology and the Weka API, the study illuminates user behaviors and energy consumption patterns, enabling household classification based on energy usage profiles. Moreover, to ensure user comfort and safety, RuleML and Apache Mahout are deployed to customize energy-saving recommendations tailored to individual preferences. By presenting a practical demonstration of smart home monitoring, this paper validates the effectiveness of the proposed approach in enhancing security, comfort, and energy conservation. This pioneering research not only showcases the transformative potential of IoT-driven energy management systems but also sets the stage for a sustainable and interconnected future.

Anomaly Detection Model of Time Segment Power Usage Behavior Using Unsupervised Learning

<p>In Taiwan, the current electricity prices for residential users remain relatively low. This results in a diminished incentive for these users to invest in energy-saving improvements. Consequently, devising strategies to encourage residential users to adopt energy-saving measures becomes a vital research area. Grounded in behavioral science, this study introduces a feasible approach where an energy management system provides alerts and corresponding energy-saving recommendations to residential users upon detecting abnormal electricity consumption behavior. To pinpoint anomalous electricity usage within specific time segments, this research employs an unsupervised machine learning method, developing an anomaly detection model for the overall electricity consumption behavior of residential users. The model focuses on analyzing 2-hour intervals of electricity consumption, enabling more effective detection of abnormal usage patterns. It is trained using power consumption data collected from five actual residential users as part of an experimental study. The results indicate that the proposed anomaly detection model achieves performance metrics such as Precision, Recall, and F1-score of 0.90 or above, showcasing its potential for practical implementation.</p> <p> </p>

Assessment of Detailed Energy Conservation Potentials: The Case of the Ethiopian Leather Industry

One of the most crucial components in any industrial operation is energy. However, the supply is not limitless. One of the key ingredients in cement production is energy, whose cost share ranges from 8 to 15% of overall production costs in developed nations but is much more in undeveloped nations. Therefore, the objective of this extensive research was to carry out a thorough energy conservation audit at the Sheba leather factory, located in the city of Mekelle, in the north region of Ethiopia. The specific objective of this research was to analyse the patterns of power consumption, identify energy-saving techniques, as well as to propose energy-saving recommendations for their implementation. It was obtained using primary and secondary data from the industry personnel. As a result, a total of 19 recommendations for energy saving were found and were forwarded for consideration. These recommendations have the potential to save a total of about $ 29900 annually, but their implementation would cost close to $ 15900, with a payback period of seven months. These recommendations also cover the utilities of the boiler, motors, blower, air compressors, cooling tower, and lighting. In order to lower their energy use, economic benefits are also considered.

Multi-sensor data fusion framework for energy optimization in smart homes

Advancements in Internet of Energy (IoE) technologies drive the development of several energy efficient frameworks for better energy optimization, economic savings, safety, and security in smart homes. However, certain challenges such as real-time operational data for each micro-moment, proper application of data fusion techniques, and end-to-end computing and deployment architecture prevent the establishment of an effective energy-efficient framework to provide personalized energy-saving recommendations. This work presents energy management for smart spaces (EMSS), the proposed energy efficiency framework implemented in an edge-cloud computing platform that fuses data from heterogeneous data sources (environmental sensors, camera, plug data, etc.) at appropriate data fusion levels and process them to generate actionable, explainable, personalized, and persuasive recommendations at the right moment. The user response to the generated recommendations triggers the actuators to perform respective energy-saving actions and provide more personalized future recommendations. Further, SMARTHome - a data generation framework based on configurable scenario files and a set of software codes was proposed to generate synthetic data with respect to different building types and micro-moments. The functionalities of the EMSS (device and user registration), user dashboard, analytics, and energy-saving recommendations were made accessible to the user through web and mobile applications. The validation analysis of the EMSS was performed by (i) comparative analysis of the machine learning and deep learning algorithms used by the decision engine to generate energy-saving recommendations and (ii) benchmarking of EMSS based on the taxonomy of data fusion-based energy efficiency frameworks for smart homes.

Techno-economic analysis of solar powered battery bank in a residential apartment

The electrical load is ever-increasing in the residential grid, there is a necessity to increase power generation to meet the demand (or) the centralized battery energy storage system can provide part of the generation. This paper describes the implementation of a centralized Solar Powered Battery Bank (SPBB) in residential apartments, which consists of solar panels and battery banks. To identify energy-saving recommendations, an energy audit is conducted using Fluke. As a result of performing quasi-dynamic load flow analysis in DIgSILENT for a realtime system with 64 residences and 47 inverter batteries, the study concludes that the centralized SPBB model reduces traditional grid power supply during peak periods. The proposed model's optimal sizing is determined using the Modified Particle Swarm Optimization technique. By using the SPBB model, the voltage stability of the fourth floor is improved from 0.8351 p.u. to 0.9566 p.u. In these models, daily energy consumption is reduced by up to 18%, with a minimum daily energy savings of 250 kWh, and total cost savings of 1491 rupees. Finally, an economic study is conducted to determine the proposed model's payback period, and comparing it with previous work shows that the proposed model is advantageous.

Enhancing Building Energy Management: Adaptive Edge Computing for Optimized Efficiency and Inhabitant Comfort

Nowadays, in contemporary building and energy management systems (BEMSs), the predominant approach involves rule-based methodologies, typically employing supervised or unsupervised learning, to deliver energy-saving recommendations to building occupants. However, these BEMSs often suffer from a critical limitation—they are primarily trained on building energy data alone, disregarding crucial elements such as occupant comfort and preferences. This inherent lack of adaptability to occupants significantly hampers the effectiveness of energy-saving solutions. Moreover, the prevalent cloud-based nature of these systems introduces elevated cybersecurity risks and substantial data transmission overheads. In response to these challenges, this article introduces a cutting-edge edge computing architecture grounded in virtual organizations, federated learning, and deep reinforcement learning algorithms, tailored to optimize energy consumption within buildings/homes and facilitate demand response. By integrating energy efficiency measures within virtual organizations, which dynamically learn from real-time inhabitant data while prioritizing comfort, our approach effectively optimizes inhabitant consumption patterns, ushering in a new era of energy efficiency in the built environment.

SIMULATION OF COMPLEX EFFECTS OF THERMAL MODERNIZATION MEASURES OF THE BUILDING TAKING INTO ACCOUNT INVESTMENT LIMITATIONS

The article simulates the impact of the complex application of energy-saving measures on the energy efficiency indicators of a public brick building. During modeling, the economic factor – capital investment in energy saving measures – was taken into account. Similar simulations are carried out during energy audits of various types of buildings. Performing energy audits of residential and public buildings in the city is, in accordance with the Law of Ukraine “On Energy Efficiency”, an important part of Ukraine’s energy conservation policy. Energy audits are required for: a) development of an energy certificate, which is required for many new and existing buildings in accordance with current legislation; b) attraction of funds from the energy efficiency fund and other funds for the needs of thermal modernization; c) obtaining reasonable indicators that will allow building owners to make energy-efficient decisions. The energy audit report contains three stages: the stage of obtaining information, the stage of analyzing information and developing energy-saving recommendations, and the stage of technical and economic substantiation of the proposed energy-saving recommendations. At the first stage, energy auditors collect raw data about buildings. Existing buildings are often characterized by the lack of insulation of opaque enclosing structures, the presence of translucent enclosing structures, the reduced heat transfer supports of which are significantly lower than the minimum permissible values, and outdated sources of heat supply. During the second stage, mathematical models are developed that allow obtaining technical and economic indicators of energy saving measures. The third stage is the substantiation of optimal measures that must satisfy technical and economic constraints. The work analyzed the dependence of the general indicator of specific energy consumption during heating and cooling of a two-story brick public building under the condition of modernization of the gas boiler, as well as insulation of the brick facades of the building with polystyrene foam plates of different thicknesses. At the same time, an analysis of both individual and complex application of the specified measures was carried out. General capital investment in thermal modernization was adopted as an economic indicator that sets limits on energy saving measures. As a result of the modeling, it was established that in order to determine the optimal measures to increase the energy efficiency of the building, it is necessary to use optimization mathematical devices that take into account the existing restrictions and can be integrated into mathematical models that calculate the basic level of energy consumption in compliance with the normative parameters of the building’s operation.

Intelligent Edge-Based Recommender System for Internet of Energy Applications

Preserving energy in households and office buildings is a significant challenge, mainly due to the recent shortage of energy resources, the uprising of the current environmental problems, and the global lack of utilizing energy-saving technologies. Not to mention, within some regions, COVID-19 social distancing measures have led to a temporary transfer of energy demand from commercial and urban centers to residential areas, causing an increased use and higher charges, and in turn, creating economic impacts on customers. Therefore, the marketplace could benefit from developing an Internet of Things ecosystem that monitors energy consumption habits and promptly recommends action to facilitate energy efficiency. This article aims to present the full integration of a proposed energy efficiency framework into the Home-Assistant platform using an edge-based architecture. End users can visualize their consumption patterns as well as ambient environmental data using the Home-Assistant user interface. More notably, explainable energy-saving recommendations are delivered to end users in the form of notifications via the mobile application to facilitate habit change. In this context, to the best of the authors’ knowledge, this is the first attempt to develop and implement an energy-saving recommender system on edge devices. Thus, ensuring better privacy preservation since data are processed locally on the edge, without the need to transmit them to remote servers, as is the case with cloudlet platforms.

INTERNET OF THINGS BASED ZIGBEE SNIFFER FOR SMART AND SECURE HOME

The aim of this research is to determine the usage of energy or power with high spectrum allocation in ZigBee Protocol with the help of clustering in IoT. This research starts presenting an overview of the broadband network energy sector and the challenges that are facing. It is observed a change on the energy policies promoting the energy efficiency, encouraging an active role of the consumer, instructing them about the importance of the consumer behavior and protecting consumer rights. Electricity is gaining room as energy source; its share will keep increasing constantly in the following decades. The objective behind this energy consumption segmentation is to be able to provide personalized recommendations to each group in order to reduce their energy consumption and the associated costs, fostering energy efficiency measures and improving the consumer engagement. The desired segmentation is obtained by an iterative process, based on computational clusters calculation (using python programming language) and finalized by a post-clustering analysis applying visualization and statistical data mining technique to detect the energy consumption and reallocate them to a more appropriate group. The K-Means clustering technique was tested and compared, giving the best prediction of accuracy 98.46% for all energy load profiles with high spectrum of 100GHz. The solution from the K-Means clustering is the one that better adapts to the segmentation sought, which is used as the base of the post-clustering stage to obtain the final energy consumption segmentation. Most of these methodologies use the absolute values in 100 kWh, as they were more focused on identify the users with higher energy savings potential. In this case, it allows personalizing energy savings recommendations according to the specific characteristics of ZigBee protocol, improving the consumer experience by being able to provide the adequate advice at the appropriate time, facts that increase the effectiveness of the energy efficiency advises’ service for future ZigBee protocol.

Smart fusion of sensor data and human feedback for personalized energy-saving recommendations

Despite the variety of sensors that can be used in a smart home or office setup, for monitoring energy consumption and assisting users to save energy, their usefulness is limited when they are not properly integrated into the daily activities of humans. Energy-saving applications in such environments can benefit from the use of sensors and actuators when data are properly fused with previous knowledge about user habits and feedback about current user preferences. In this article, we present an online recommender system implemented in the EM3 platform, a platform for Consumer Engagement Toward Energy-Saving Behavior. The recommender system uniquely fuses sensors’ data with user habits and user feedback and provides personalized recommendations for energy efficiency at the right moment. The user response to the recommendations directly triggers actuators that perform energy-saving actions and is recorded and processed for refining future recommendations. The EM3 recommendation engine continuously evaluates the three inputs (i.e. sensor data, user habits, user feedback) and identifies the micro-moments that maximize the need for the recommended action and thus the recommendation acceptance. We evaluate the efficiency of the proposed recommender system, which is based on a stacked-LSTM for fusing multi-sensor data streams, in several scenarios, and the observed accuracy on predicting the right moment to send a recommendation to the user ranged from 93% to 97%.

Trends and Future Directions in Crop Energy Analyses: A Focus on Iran

This systematic review critically analyzes the literature on the study of energy-use patterns in agricultural crop systems in Iran. We examine the relevant methodologies and research trends from 2008 to 2019, a particularly active and productive period. Initially, we find researchers using energy audits and regression modeling to estimate energy-use patterns. Then economic and environmental-emissions audits are more commonly incorporated into analyses. Finally, the application of different Artificial Intelligence (AI) methods are observed in papers. The main focus of this study is on energy-use patterns, economic modelling, and environmental emissions. We then address critical issues, including sample size, energy equivalents, and additional practical energy-saving recommendations which can be considered by researchers in future analyses. The application of AI in the analysis of agricultural systems, and how it can be used to achieve sustainable agriculture, is discussed with the aim of providing guidelines for researchers interested in energy flow in agricultural systems, especially in Iran. To achieve sustainable agriculture systems, we recommend more attention be given toward considering the impact of social factors in addition to energy, environmental and economic factors. Finally, this review should guide other researchers in choosing appropriate crop types and regions in need study to avoid repetitive studies.

A Deep-Reinforcement-Learning-Based Recommender System for Occupant-Driven Energy Optimization in Commercial Buildings

In this article, we present recEnergy , a recommender system for reducing energy consumption in commercial buildings with human-in-the-loop. We formulate the building energy optimization problem as a Markov decision process, show how deep reinforcement learning can be used to learn energy-saving recommendations, and effectively engage occupants in energy-saving actions. recEnergy is a recommender system that learns actions with high-energy-saving potential, actively distributes recommendations to occupants in a commercial building, and utilizes feedback from the occupants to learn better energy-saving recommendations. Over a four-week user study, four different types of energy-saving recommendations were trained and learned. recEnergy improves building energy reduction from a baseline saving (passive-only strategy) of 19%–26%.

HEMS-IoT: A Big Data and Machine Learning-Based Smart Home System for Energy Saving

Energy efficiency has aroused great interest in research worldwide, because energy consumption has increased in recent years, especially in the residential sector. The advances in energy conversion, along with new forms of communication, and information technologies have paved the way for what is now known as smart homes. The Internet of Things (IoT) is the convergence of various heterogeneous technologies from different application domains that are used to interconnect things through the Internet, thus allowing for the detection, monitoring, and remote control of multiple devices. Home automation systems (HAS) combined with IoT, big data technologies, and machine learning are alternatives that promise to contribute to greater energy efficiency. This work presents HEMS-IoT, a big data and machine learning-based smart home energy management system for home comfort, safety, and energy saving. We used the J48 machine learning algorithm and Weka API to learn user behaviors and energy consumption patterns and classify houses with respect to energy consumption. Likewise, we relied on RuleML and Apache Mahout to generate energy-saving recommendations based on user preferences to preserve smart home comfort and safety. To validate our system, we present a case study where we monitor a smart home to ensure comfort and safety and reduce energy consumption.

Achieving Domestic Energy Efficiency Using Micro-Moments and Intelligent Recommendations

Excessive domestic energy usage is an impediment towards energy efficiency. Developing countries are expected to witness an unprecedented rise in domestic electricity in the forthcoming decades. A large amount of research has been directed towards behavioral change for energy efficiency. Thus, it is prudent to develop an intelligent system that combines the proper use of technology with behavior change research in order to sustainably transform end-user behavior at a large scale. This paper presents an overview of our AI-based energy efficiency framework for domestic applications and explains how micro-moments can provide an accurate understanding of user behavior and lead to more effective recommendations. Micro-moments are short-term events at which an energy-saving recommendation is presented to the consumer. They are detected using a variety of sensing modules placed at prominent locations in the household. A supervised machine learning classifier is then used to analyze the acquired micro-moments, identify abnormalities, and formulate a list of energy-saving recommendations. Each recommendation is presented through the end-user mobile application. The results so far include a mobile application in the front-end and a set of sensing modules in the backend that comprise, an ensemble bagging-trees micro-moment classifier (achieving up to 99.64% accuracy and 98.8% F-score), and a recommendation engine.

A study on the impact of data sampling rates on load signature event detection

The analysis of electrical load signatures is an enabling technology for many applications, such as ambient assisted living or energy-saving recommendations. Through the digitalization of electricity metering infrastructure, meter reading intervals are gradually becoming more frequent than the traditional once-per-year reporting. In fact, across smart meter generations, samples were initially reported in 15-min intervals, more recently once per second, and even newer devices capture readings at rates on the order of several kilohertz. The advantages of using such high sampling rates have, however, not been unambiguously demonstrated in literature. We thus choose a widely considered application scenario of energy data analytics, event detection, and assess the impact of the sampling rate choice on the correct event recognition rate. More specifically, we compare the accuracy of two event detection algorithms with respect to the resolution of their input data. The results of our analysis hint at a non-linear relation between accuracy and data resolution, yet also indicate that most event occurrences can be correctly determined when using a sampling rate of approximately 1 kHz, with only minimal improvements achievable through higher rates.

属性や電気使用傾向による節電率の差異

In this paper, we studied differences in the electricity saving rates across households, observed in the summer months of July through September after the Great East Japan Earthquake. To explain what factors carry different influences on the electricity saving rates, we ran panel data regression models using the electricity consumption billing data in the summer months, July through September, over the five years of 2010-2014 for 910 households in the Tokyo and Kansai areas. Specifically, we concentrated on two main explanatory factors: household attributes that include household size and income, and electricity consumption tendencies characterized by average consumption level and its standard deviation. We found that, while some household attributes such as age and floor space have statistically significant linear relationships with the saving rates, households with higher average consumption can have higher saving rates, implying the electricity consumption data acquired by smart meters may be among the key determinants for tailored energy-saving recommendations.

Energy Efficiency in Small and Medium-Sized Manufacturing Firms: Order Effects and the Adoption of Process Improvement Recommendations

In many manufacturing operations, profitable energy efficiency opportunities remain unexploited. Although previous studies have tried to explain the underinvestment, we focus on how the way in which a portfolio of opportunities is presented in a list affects adoption decisions. We use information on over 100,000 energy-saving recommendations made to more than 13,000 small and medium-sized manufacturing firms under the Industrial Assessment Centers program of the U.S. Department of Energy. We find that adoption rates are higher for initiatives appearing early in a list of recommendations. This sequence effect is consistent and large: simply moving a recommendation one position lower has the same effect on average as increasing up-front implementation cost by at least 17% from the average value. Given this impact of sequence on adoption of individual recommendations, we utilize variations within our data to examine how various sequencing approaches affect adoption at the portfolio level. Sequences in which recommendations are listed from best to worst payback achieve higher potential energy savings given the investments in energy efficiency made by the firms. We also observe a choice overload effect at the portfolio level, but the magnitude of this effect is small.

A Case Study for Energy Auditing Using an Interactive Energy Balance

ABSTRACT The two previous articles in this series have described how to create an energy balance (Analyzing Facility Energy Use: A Balancing Act) [1], and how to expand the basic energy balance spreadsheet to include a number of economic analysis measures (Energy Balancing—How to Use the Energy Balance Data You Have Collected to Make Financial Decisions) [2]. Simultaneously, another article (An Interactive Energy Balance: A Case Study) [3] was published which emphasized concepts and improved the ideas of the previous ones through an interactive, user-friendly software. In this article—the third in the series—we describe this new interactive software that allows the user to perform an energy balance for a facility more easily and efficiently. This new, interactive software provides a graphical user interface, or GUI, to expedite the entry of data, using menus whenever possible. Large amounts of data on facility energy bills, facility equipment, and operating characteristics are still required, but the new interactive software greatly speeds up this data entry process and reduces the chances for data entry errors. In addition, the process of adjusting facility equipment and operating characteristics to match the actual energy consumption is improved and compressed so that this critical task is also performed with less manual effort. All the energy consumption data are displayed in graphs and pie charts. Moreover, calculations of potential energy savings recommendations are performed for each type of equipment considered. The end result is an energy balance software tool that is much easier to use than any of our previous Excel spreadsheets. This increased ease of use makes it much more likely that other people will use this tool to assist them in their energy auditing studies, making decisions to purchase new equipment, or upgrading less efficient equipment. The article concludes with a case study that provides insight on the faster data entry and balancing process, graphical display, and recommendations, as well as further demonstrating the value of the energy balance approach.