scholarly journals Admission Control in Home Energy Management Systems Using Theatre and Hybrid Actors

Modelling ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 288-307
Author(s):  
Franco Cicirelli ◽  
Libero Nigro
Keyword(s):  
Energy Consumption ◽  
Energy Management ◽  
Admission Control ◽  
Timed Automata ◽  
Methodological Approach ◽  
Monitoring And Control ◽  
Actor Model ◽  
Cumulative Energy ◽  
Home Energy Management ◽  
Hybrid Actors

The goal of a Home Energy Management System (HEMS) is that of purposely shaping the cumulative energy consumption curves of domestic appliances by imposing suitable monitoring and control policies. The development of HEMS, like the development of general Cyber-Physical Systems (CPSs), is challenging, as it requires the exploitation of suitable methodological approaches which are able to deal jointly with the continuous and discrete behaviours of a CPS. In this paper, a methodological approach for HEMS is advocated which relies on the use of the Theatre actor system with hybrid actors. As a key feature, Theatre enables the same actor model to be used during the analysis, design, prototyping and implementation phases of the system. For property assessment, a Theatre model is reduced to Uppaal hybrid timed automata for analysis by statistical model checking. As a significant modelling example, a HEMS is proposed which implements an admission control strategy able to maintain the in-home energy consumption under a given threshold. Instead of reacting to an overload condition, the strategy is able to prevent an overload upfront by predicting the effect that the admission of a new load will have on the consumption curve of the whole system.

scholarly journals IoT Operating System Based Fuzzy Inference System for Home Energy Management System in Smart Buildings

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2802 ◽  
Author(s):  
Qurat-ul Ain ◽  
Sohail Iqbal ◽  
Safdar Khan ◽  
Asad Malik ◽  
Iftikhar Ahmad ◽  
...  
Keyword(s):  
Operating System ◽  
Energy Consumption ◽  
Energy Management ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Residential Buildings ◽  
Inference System ◽  
Intelligent Sensors ◽  
Home Energy Management ◽  
User Comfort

Energy consumption in the residential sector is 25% of all the sectors. The advent of smart appliances and intelligent sensors have increased the realization of home energy management systems. Acquiring balance between energy consumption and user comfort is in the spotlight when the performance of the smart home is evaluated. Appliances of heating, ventilation and air conditioning constitute up to 64% of energy consumption in residential buildings. A number of research works have shown that fuzzy logic system integrated with other techniques is used with the main objective of energy consumption minimization. However, user comfort is often sacrificed in these techniques. In this paper, we have proposed a Fuzzy Inference System (FIS) that uses humidity as an additional input parameter in order to maintain the thermostat set-points according to user comfort. Additionally, we have used indoor room temperature variation as a feedback to proposed FIS in order to get the better energy consumption. As the number of rules increase, the task of defining them in FIS becomes time consuming and eventually increases the chance of manual errors. We have also proposed the automatic rule base generation using the combinatorial method. The proposed techniques are evaluated using Mamdani FIS and Sugeno FIS. The proposed method provides a flexible and energy efficient decision-making system that maintains the user thermal comfort with the help of intelligent sensors. The proposed FIS system requires less memory and low processing power along with the use of sensors, making it possible to be used in the IoT operating system e.g., RIOT. Simulation results validate that the proposed technique reduces energy consumption by 28%.

scholarly journals A Survey of Communications and Networking Technologies for Energy Management in Buildings and Home Automation

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Aravind Kailas ◽  
Valentina Cecchi ◽  
Arindam Mukherjee
Keyword(s):  
Energy Consumption ◽  
Power Consumption ◽  
Energy Management ◽  
Smart Grids ◽  
Energy Demand ◽  
Demand Curve ◽  
Future Research ◽  
Energy Awareness ◽  
Home Energy Management ◽  
Networking Technologies

With the exploding power consumption in private households and increasing environmental and regulatory restraints, the need to improve the overall efficiency of electrical networks has never been greater. That being said, the most efficient way to minimize the power consumption is by voluntary mitigation of home electric energy consumption, based on energy-awareness and automatic or manual reduction of standby power of idling home appliances. Deploying bi-directional smart meters and home energy management (HEM) agents that provision real-time usage monitoring and remote control, will enable HEM in “smart households.” Furthermore, the traditionally inelastic demand curve has began to change, and these emerging HEM technologies enable consumers (industrial to residential) to respond to the energy market behavior to reduce their consumption at peak prices, to supply reserves on a as-needed basis, and to reduce demand on the electric grid. Because the development of smart grid-related activities has resulted in an increased interest in demand response (DR) and demand side management (DSM) programs, this paper presents some popular DR and DSM initiatives that include planning, implementation and evaluation techniques for reducing energy consumption and peak electricity demand. The paper then focuses on reviewing and distinguishing the various state-of-the-art HEM control and networking technologies, and outlines directions for promoting the shift towards a society with low energy demand and low greenhouse gas emissions. The paper also surveys the existing software and hardware tools, platforms, and test beds for evaluating the performance of the information and communications technologies that are at the core of future smart grids. It is envisioned that this paper will inspire future research and design efforts in developing standardized and user-friendly smart energy monitoring systems that are suitable for wide scale deployment in homes.

scholarly journals Design of Ensemble Forecasting Models for Home Energy Management Systems

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7664
Author(s):  
Karol Bot ◽  
Samira Santos ◽  
Inoussa Laouali ◽  
Antonio Ruano ◽  
Maria da Graça Ruano
Keyword(s):  
Energy Consumption ◽  
Energy Management ◽  
Simple Procedure ◽  
Ensemble Forecasting ◽  
Management Systems ◽  
Energy Forecasting ◽  
Energy Management Systems ◽  
Ensemble Techniques ◽  
Home Energy Management ◽  
Home Energy Management System

The increasing levels of energy consumption worldwide is raising issues with respect to surpassing supply limits, causing severe effects on the environment, and the exhaustion of energy resources. Buildings are one of the most relevant sectors in terms of energy consumption; as such, efficient Home or Building Management Systems are an important topic of research. This study discusses the use of ensemble techniques in order to improve the performance of artificial neural networks models used for energy forecasting in residential houses. The case study is a residential house, located in Portugal, that is equipped with PV generation and battery storage and controlled by a Home Energy Management System (HEMS). It has been shown that the ensemble forecasting results are superior to single selected models, which were already excellent. A simple procedure was proposed for selecting the models to be used in the ensemble, together with a heuristic to determine the number of models.

scholarly journals Human Comfort-Based-Home Energy Management for Demand Response Participation

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2463 ◽  
Author(s):  
Herie Park
Keyword(s):  
Energy Consumption ◽  
Energy Management ◽  
Demand Response ◽  
Residential Building ◽  
Limited Participation ◽  
Human Comfort ◽  
Control Approach ◽  
Home Energy Management ◽  
Lighting Systems ◽  
Home Energy Management System

The residential building sector is encouraged to participate in demand response (DR) programs owing to its flexible and effective energy resources during peak hours with the help of a home energy management system (HEMS). Although the HEMS contributes to reducing energy consumption of the building and the participation of occupants in energy saving programs, unwanted interruptions and strict guidance from the system cause inconvenience to the occupants further leading to their limited participation in the DR programs. This paper presents a human comfort-based control approach for home energy management to promote the DR participation of households. Heating and lighting systems were chosen to be controlled by human comfort factors such as thermal comfort and visual comfort. Case studies were conducted to validate the proposed approach. The results showed that the proposed approach could effectively reduce the energy consumption during the DR period and improve the occupants’ comfort.

Robust anticipative energy management system: Application of a smart building platform

2016 ◽  
Vol 38 (2) ◽  
pp. 226-248 ◽  
Author(s):  
Minh-Hoang Le ◽  
Stephane Ploix
Keyword(s):  
Energy Consumption ◽  
Energy Management ◽  
Optimization Problem ◽  
Energy Allocation ◽  
Electricity Production ◽  
Variable Cost ◽  
Parametric Uncertainties ◽  
Management Problem ◽  
Weather Forecasts ◽  
Home Energy Management

Robust energy management in buildings is addressed in this paper. The energetic impact of buildings in the current energetic context is first presented. Then the studied optimization problem is defined as the optimal management of production and consumption activities in buildings. A scheduling problem is identified to adjust the energy consumption to both the energy cost and the user’s comfort. The available flexibility of the services provided by domestic appliances is used to compute optimal energy plans. These flexibilities are associated to time windows or heating storage abilities. A constraints formulation of the energy allocation problem is given. A derived mixed linear program is used to solve this problem. The energy consumption in houses is very dependent on uncertain data such as weather forecasts and inhabitants’ activities. Parametric uncertainties are introduced in the home energy management problem in order to provide robust energy allocation. Robust linear programming is implemented. A scenario-based approach is implemented to face this robust optimization problem. Practical application: Because of the increasing part of renewable energy in electricity production, which is difficult to control, consumers will have to become more involved in the grid management. This paper states the problem of energy management in buildings and describes the optimization problem defined to adjust the energy consumption of buildings to production constraints. This decision system is based on the weather forecasts and a variable cost of electricity. Parametric uncertainties on the data are taken into account in order to propose robust energy planning in which a performance is guaranteed over the expected data.

scholarly journals Wireless Home Energy Management System with Smart Rule-Based Controller

2020 ◽  
Vol 10 (13) ◽  
pp. 4533
Author(s):  
Hussain Shareef ◽  
Eslam Al-Hassan ◽  
Reza Sirjani
Keyword(s):  
Energy Consumption ◽  
Energy Management ◽  
Residential Buildings ◽  
Public Utility ◽  
Renewable Energy Resources ◽  
Rule Based ◽  
Central Controller ◽  
Home Energy Management ◽  
Daily Energy ◽  
Utility Services

Despite the increasing utilization of renewable energy resources, such as solar and wind energy, most residential buildings still rely on conventional energy supply by public utility services. Such utility services often use time-of-use energy pricing, which compels residential consumers to reduce their energy usage. This paper presents a wireless home energy management (HEM) system that enables the automatic control of home appliances to reduce energy consumption to assist such energy users. The system consists of multiple smart sockets that measure the energy that is consumed by the connected appliances and are capable of implementing on/off commands. The system includes other support components for supplying data to a central controller, which utilizes a rule-based HEM algorithm. The control rules were designed, such that the lifestyle of the user would be preserved while the energy consumption and daily energy cost were reduced. The experimental results showed that the central controller could effectively receive data and control multiple devices. The system was also found to afford significant reductions of 23.5 kWh and $2.898 in the total daily energy consumption and bill of the considered household setup, respectively. The proposed HEM system promises to be particularly useful for households with a high daily energy consumption.

scholarly journals Optimization of Demand-Response-Based Intelligent Home Energy Management System with Binary Backtracking Search Algorithm

Information ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 395
Author(s):  
Suhaib N. Abdul Latif ◽  
Jinjing Shi ◽  
Hasnain Ali Salman ◽  
Yongze Tang
Keyword(s):  
Energy Consumption ◽  
Energy Management ◽  
Demand Response ◽  
Search Algorithm ◽  
Optimal Schedule ◽  
Backtracking Search ◽  
Backtracking Search Algorithm ◽  
Intelligent Home ◽  
Home Energy Management ◽  
Demand Limit

In many nations, limited power from providers and an increase in demand for electricity have created new opportunities that can be used by home energy management systems (HEMSs) systems to enforce proper use of energy. This paper presents a virtual intelligent home with demand response (DR) model home appliances that have an inverter air conditioner, water pump, washing machine, and inverter refrigerator. A binary backtracking search algorithm (BBSA) is proposed to introduce the optimal schedule controller. With the proposed BBSA schedule controller, the highest energy consumption during DR can be reduced by 33.84% during the weekends and by 30.4% daily during the weekdays. The results indicate the effectiveness of the proposed HEMS. Additionally, the model can control the appliances and maintain total residential energy consumption below the defined demand limit.

scholarly journals Utilization of ICT and AI techniques in harnessing residential energy consumption for an energy-aware smart city: A review

2021 ◽  
Vol 8 (7) ◽  
pp. 50-66
Author(s):  
Mahmood et al. ◽  
Keyword(s):  
Energy Consumption ◽  
Power Consumption ◽  
Information And Communication Technologies ◽  
Energy Management ◽  
Optimization Problems ◽  
Consumption Patterns ◽  
Management Systems ◽  
Residential Sector ◽  
Energy Management Systems ◽  
Home Energy Management

Fusion of Information and Communication Technologies (ICT) in traditional grid infrastructure makes it possible to share certain messages and information within the system that leads to optimized use of energy. Furthermore, using Computational Intelligence (CI) in the said domain opens new horizons to preserve electricity as well as the price of consumed electricity effectively. Hence, Energy Management Systems (EMSs) play a vital role in energy economics, consumption efficiency, resourcefulness, grid stability, reliability, and scalability of power systems. The residential sector has its high impact on global energy consumption. Curtailing and shifting load of the residential sector can result in solving major global problems and challenges. Moreover, the residential sector is more flexible in reshaping power consumption patterns. Using Demand Side Management (DSM), end users can manipulate their power consumption patterns such that electricity bills, as well as Peak to Average Ratio (PAR), are reduced. Therefore, it can be stated that Home Energy Management Systems (HEMSs) is an important part of ground-breaking smart grid technology. This article gives an extensive review of DSM, HEMS methodologies, techniques, and formulation of optimization problems. Concluding the existing work in energy management solutions, challenges and issues, and future research directions are also presented.

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