Scheduling Of Home Appliances Research Articles

Navigating urban day-ahead energy management considering climate change toward using IoT enabled machine learning technique: Toward future sustainable urban

The rapid evolution of technology and urban population growth has led to a significant increase in energy consumption, particularly in electricity production. However, the ability to meet energy demands, especially during peak periods, is constrained by various factors including environmental concerns, technological limitations, and economic constraints. Effective energy management strategies are crucial to mitigate these challenges and ensure a sustainable energy future. One promising approach is the implementation of demand response programs. In this context, we explore the potential of Home Energy Management Systems (HEMS) leveraging modern Internet of Things (IoT) technology. HEMS systems are at the forefront of research and development in the field of smart grids (SGs), offering capabilities in demand-side management and power availability control. These systems achieve efficient energy usage by optimizing the operation of home appliances (HAs). To further enhance the performance of HEMS, this study introduces a novel metaheuristic known as the Grey Wolf and Crow Search Optimization Algorithm (GWCSOA). This algorithm is employed to determine the optimal scheduling of HAs within the HEMS framework. Additionally, we utilize MATLAB and ThingSpeak modules to implement and test the HEMS design. The proposed HEMS, integrated with GWCSOA, demonstrates remarkable capabilities in reducing daily power costs, minimizing the peak-to-average ratio (PAR), and enhancing consumer satisfaction. The findings of this study indicate that the devised system holds the promise of substantially diminishing both power expenses and microgrid emissions. Through the optimization of Home Energy Management Systems (HEMS) operations, this methodology plays a vital role in realizing a sustainable urban energy ecosystem. This aligns seamlessly with the overarching objectives of combating climate change and securing the enduring sustainability of our cities. The proposed HEMS, integrated with GWCSOA, demonstrates remarkable capabilities in reducing daily power costs by an average of 25.98 %, minimizing the peak-to-average ratio (PAR) by 30 %, and enhancing consumer satisfaction. The outcomes of this research reveal that the developed system has the potential to significantly reduce both power costs and microgrid emissions. By optimizing HEMS operations, this approach contributes to achieving a sustainable urban energy ecosystem, aligning with the goals of addressing climate change and ensuring the future sustainability of our cities.

Optimal integration of solar home systems and appliance scheduling for residential homes under severe national load shedding

In developing countries like South Africa, users experienced more than 1 030 hours of load shedding outages in just the first half of 2023 due to inadequate power supply from the national grid. Residential homes that cannot afford to take actions to mitigate the challenges of load shedding are severely inconvenienced as they have to reschedule their demand involuntarily. This study presents optimal strategies to guide households in determining suitable scheduling and sizing solutions for solar home systems to mitigate the inconvenience experienced by residents due to load shedding. To start with, we predict the load shedding stages that are used as input for the optimal strategies by using the K-Nearest Neighbour (KNN) algorithm. Based on an accurate forecast of the future load shedding patterns, we formulate the residents’ inconvenience and the loss of power supply probability during load shedding as the objective function. When solving the multi-objective optimisation problem, four different strategies to fight against load shedding are identified, namely (1) optimal home appliance scheduling (HAS) under load shedding; (2) optimal HAS supported by solar panels; (3) optimal HAS supported by batteries, and (4) optimal HAS supported by the solar home system with both solar panels and batteries. Among these strategies, appliance scheduling with an optimally sized 9.6 kWh battery and a 2.74 kWp panel array of five 550 Wp panels, eliminates the loss of power supply probability and reduces the inconvenience by 92% when tested under the South African load shedding cases in 2023.

A novel optimal framework for scheduling rooftop solar home appliances considering electricity, real pricing and user comfort

With an increase in electrification trends, the energy consumption of households is expected to increase significantly in the near future. To reduce electricity bills in the electrified era, management of the demand side is inevitable. Different demand response programs, such as real-time pricing can be introduced from the grid side. The consumption side needs to adjust its consumption patterns in accordance with the price signals. Therefore, an innovative home appliance scheduling method is proposed considering the rooftop solar panels as part of energy suppliers. First, an optimization problem is formulated considering different objectives such as energy price, consumer satisfaction, and peak-to-average load ratio. Then, home appliances are divided into three major categories (shiftable, flexible, and normally operated) to adjust schedules of different load types. Then, the developed model is solved using the bald eagle search optimization algorithm (BESOA). Finally, the performance of the proposed BESOA-based method is compared with other well-known heuristic/meta-heuristic methods. In addition, an analysis of different equipment’s maximal operation time is conducted for all the methods. In order to provide feedback on user comfort levels, digital twin technology is used allowing the user to adjust the scheduling of the appliances to ensure that they are comfortable. By leveraging digital twin technology, users would be able to optimize the scheduling of their rooftop solar home appliances to maximize efficiency and minimize costs. Simulation results have shown the superiority of the BESOA method over other methods in terms of daily average energy cost, peak-to-average load ratio, and consumer satisfaction.

Development of two stage optimization-based demand response technique for smart homes under real time pricing

Residential load management deals with two major objectives viz. minimizing the cost of monthly electricity bill and peak demand of power consumption. Both objectives can be achieved by effective operational scheduling of smart home appliances. These two objectives are conflicting in nature because rescheduling of appliances in order to minimize one objective may result in the rise of another. To achieve both objectives concurrently, an algorithm is suggested in this paper based on artificial intelligent techniques like cuckoo search, hybrid GA-PSO, and adaptive cuckoo search. The proposed algorithm is tested successfully on seven households of different monthly power consumption and real data of dynamic pricing options for electricity applicable in two utilities. To reduce the risk associated with real-time price, a cost function based on inclining block rates (IBR) is also suggested for both the utilities. A novel approach to find the threshold limit of hourly power consumption is also suggested in this paper. The proposed algorithm solves the optimization problem in two stages and validates its performance by successfully achieving both objectives simultaneously.

Smart Home Appliances’ Scheduling by Two-Stage Optimization with Real-Time Price Model

Residential load control focuses on reducing both the monthly electricity expense and the peak demand for electricity. The efficient scheduling of smart home appliances’ operational procedures can accomplish both goals. Because rescheduling appliances to reduce one goal can lead to an increase in the other, these two goals are inherently in conflict with one another. This work proposes an algorithm utilizing artificial intelligence methods to accomplish both goals simultaneously. The proposed method has been successfully tested on real data of energy dynamic pricing options applicable in two utilities, namely Alectra Utilities Corp., Canada, and ComEd Northern Illinois Power Company, serving residential consumers with varying monthly power use. It is also proposed that both utilities use a cost function that is based on real-time prices to mitigate the risks associated with real-time pricing. In addition, this research proposes a novel method for determining the absolute maximum hourly power usage. The suggested algorithm accomplishes its dual goals at once as proof of its efficacy in solving optimization problems.

Digital twin application for reinforcement learning based optimal scheduling and reliability management enhancement of systems

Increasing populations and economic expansion have substantially increased the energy requirements of residential consumers. Energy storage system (ESS) and distributed generation (DGs) are key tools for tackling this problem in smart homes. This study investigates the cost of electricity for residential consumers as a result of the combination of distributed photovoltaics (PVs) and ESSs for IoT-based smart home. Moreover, this paper examines energy management advantages due to bidirectional energy flow (H2G). In order to formulate the home energy management issue, PV and ESS end-user satisfaction limitations are taken into account. This study exploits a Q value-enabled reinforcement learning (RL) method to optimize home appliance scheduling (HAS) according to end-user priority. According to simulation outcomes, the suggested scheduling for household appliances performs well, and demand response (DR) measures have been implemented. It can be seen that the cost of electricity consumption as well as the uncertainty of the system have decreased in digital twin real-based application.

Multi-Criteria Energy Management with Preference Induced Load Scheduling Using Grey Wolf Optimizer

Minimizing energy costs while maintaining consumer satisfaction is a very challenging task in a smart home. The contradictory nature of these two objective functions (cost of energy and satisfaction level) requires a multi-objective problem formulation that can offer several trade-off solutions to the consumer. Previous works have individually considered the cost and satisfaction, but there is a lack of research that considers both these objectives simultaneously. Our work proposes an optimum home appliance scheduling method to obtain an optimum satisfaction level with a minimum cost of energy. To achieve this goal, first, an energy management system (EMS) is developed using a rule-based algorithm to reduce the cost of energy by efficient utilization of renewable energy resources and an energy storage system. The second part involves the development of an optimization algorithm for optimal appliance scheduling based on consumer satisfaction level, involving their time and device-based preferences. For that purpose, a multi-objective grey wolf accretive satisfaction algorithm (MGWASA) is developed, with the aim to provide trade-off solutions for optimal load patterns based on cost per unit satisfaction index (Cs_index) and percentage satisfaction (%S). The MGWASA is evaluated for a grid-connected smart home model with EMS. To ensure the accuracy of the numerical simulations, actual climatological data and consumer preferences are considered. The Cs_index is derived for six different cases by simulating (a) optimal load, (b) ideal load, and (c) base (random) load, with and without EMS. The results of MGWASA are benchmarked against other state-of-the-art optimization algorithms, namely, binary non-dominated sorting genetic algorithm-2 (NSGAII), multi-objective binary particle swarm optimization algorithm (MOBPSO), Multi-objective artificial bee colony (MOABC), and multi-objective evolutionary algorithm (MOEA). With the proposed appliance scheduling technique, a % reduction in annual energy cost is achieved. MGWASA yields Cs_index at 0.049$ with %S of 97%, in comparison to NSGAII, MOBPSO, MOABC, and MOEA, which yield %S of 95%, 90%, 92%, and 94% at 0.052$, 0.048$, 0.0485$, and 0.050$, respectively. Moreover, various related aspects, including energy balance, PV utilization, energy cost, net present cost, and cash payback period, are also analyzed. Lastly, sensitivity analysis is carried out to demonstrate the impact of any future uncertainties on the system inputs.

Power Scheduling with Max User Comfort in Smart Home: Performance Analysis and Tradeoffs

The smart grid has enabled users to control their home energy more effectively and efficiently. A home energy management system (HEM) is a challenging task because this requires the most effective scheduling of intelligent home appliances to save energy. Here, we presented a meta-heuristic-based HEM system that integrates the Greywolf Algorithm (GWA) and Harmony Search Algorithms (HSA). Moreover, a fusion initiated on HSA and GWA operators is used to optimize energy intake. Furthermore, many knapsacks are being utilized to ensure that peak-hour load usage for electricity customers does not surpass a certain edge. Hybridization has proven beneficial in achieving numerous objectives simultaneously, decreasing the peak-to-average ratio and power prices. Widespread MATLAB simulations are cast-off to evaluate the routine of the anticipated method, Harmony GWA (HGWA). The simulations are for a multi-family housing complex outfitted with various cool gadgets. The simulation results indicate that GWA functions better regarding cost savings than HSA. In reputes of PAR, HSA is significantly more effective than GWA. The suggested method reduces costs for single and ten-house construction by up to 2200.3 PKR, as opposed to 503.4 in GWA, 398.10 in HSA and 640.3 in HGWA. The suggested approach performed better than HSA and GWA in PAR reduction. For single-family homes in HGWA, GWA and HSA, the reduction in PAR is 45.79%, 21.92% and 20.54%, respectively. The hybrid approach, however, performs better than the currently used nature-inspired techniques in terms of Cost and PAR.

Optimal Load Scheduling of Home Appliances Considering Operation Conditions

To reduce energy consumption arising from increasing energy efficiency in response to energy depletion around the world, energy price rises, climate change, and accidents of electric power are cooperating simultaneously. Recognizing the seriousness of the above-mentioned problems, feasible and effective policies for reducing greenhouse gas have been promoted in developed countries since the 2000s. Moreover, industry and academia are actively researching to develop energy-efficient and eco-friendly technologies, respectively. This study proposes an optimal model for scheduling home appliances that minimizes power costs by assuming a smart-home environment with smart metering and advanced metering infrastructure. In addition, a case-study was performed using actual data from South Korea, and sensitivity analysis was performed according to changes in parameters. The experiment considered possible real-life situations, such as an increase and decrease in power cost and a limitation in power usage, and proved that the proposed model was excellent to establish a power schedule for home appliances. This research result seems to serve as a guideline in relation to the control of home appliances to reduce power and smart homes.

A Novel Solution for Day-Ahead Scheduling Problems Using the IoT-Based Bald Eagle Search Optimization Algorithm

Advances in technology and population growth are two factors responsible for increasing electricity consumption, which directly increases the production of electrical energy. Additionally, due to environmental, technical and economic constraints, it is challenging to meet demand at certain hours, such as peak hours. Therefore, it is necessary to manage network consumption to modify the peak load and tackle power system constraints. One way to achieve this goal is to use a demand response program. The home energy management system (HEMS), based on advanced internet of things (IoT) technology, has attracted the special attention of engineers in the smart grid (SG) field and has the tasks of demand-side management (DSM) and helping to control equality between demand and electricity supply. The main performance of the HEMS is based on the optimal scheduling of home appliances because it manages power consumption by automatically controlling loads and transferring them from peak hours to off-peak hours. This paper presents a multi-objective version of a newly introduced metaheuristic called the bald eagle search optimization algorithm (BESOA) to discover the optimal scheduling of home appliances. Furthermore, the HEMS architecture is programmed based on MATLAB and ThingSpeak modules. The HEMS uses the BESOA algorithm to find the optimal schedule pattern to reduce daily electricity costs, reduce the PAR, and increase user comfort. The results show the suggested system’s ability to obtain optimal home energy management, decreasing the energy cost, microgrid emission cost, and PAR (peak to average ratio).

Multi-objective scheduling of IoT-enabled smart homes for energy management based on Arithmetic Optimization Algorithm: A Node-RED and NodeMCU module-based technique

The home energy management system (HEMS) based on advanced internet of things (IoT) technology has attracted the special attention of engineers in the field of smart grid (SG), which has the task of the demand side management (DSM) and helps to control the equality between demand and electricity supply. The main performance of HEMS is based on the optimal scheduling of home appliances because it manages power consumption by automatically controlling the loads and transferring them from peak hours to off-peak hours. This paper presents a multi-objective version of a newly introduced metaheuristic, called Arithmetic Optimization Algorithm (AOA) to discover optimal scheduling of the home appliances, which is called Multi-Objective Arithmetic Optimization Algorithm (MOAOA). Furthermore, the HEMS architecture has been programmed based on the Raspberry Pi minicomputer with Node-RED and NodeMCU modules. HEMS uses the MOAOA algorithm to find the optimal schedule pattern to reduce daily electricity costs, reduce the peak to average ratio (PAR), and increase user comfort (UC). Real-time pricing (RTP) and critical peak pricing (CPP) signals are presumed as energy tariffs. Simulations are performed in two different scenarios: (I) appliance scheduling scheme and (II) appliance scheduling scheme with the integration of renewable energy sources (RES). The results of MOAOA are compared with Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Gray Wolf Optimizer (MOGWO), and Multi-Objective Antlion optimization (MOALO) algorithms. The results demonstrate that the use of the presented scheme remarkably reduces the cost of electricity consumption as well as PAR, in addition to the integration of MOAOA with RES, which greatly increases user comfort.

Optimization scheduling of home appliances in smart home: A model based on a niche technology with sharing mechanism

Smart appliances bring convenience to people’s lives, but the high electricity cost and high peak load are still drawbacks. The scheduling optimization of household electricity tasks can effectively alleviate those drawbacks. Therefore, this paper proposes a scheduling optimization model of smart home appliances to reduce the load peak value and electricity cost. Herein, the electricity tasks are defined by the set theory and classified by the operational characteristics of the loads. Then, a hybrid coding genetic algorithm based on the niche technology is proposed for scheduling optimization problems of electricity tasks. Different coding and cross-mutation operation strategies are designed for different electricity tasks. Finally, a tracing Pareto method based on weighted sum approach is proposed to solve the multi-objective optimization problem. The experimental results show that the proposed algorithm has a good performance in reducing load peaks and electricity costs. In cases A, B, and C, the load peak value is reduced by 54.7%, 45.7%, and 20.7%, respectively; the electricity cost is reduced by 5.98%, 12.00%, and 16.12%, respectively. Furthermore, a series of Pareto optimal solutions are also identified, which can help users better understand the trade-off relation between the load peak value and electricity cost.

A Shrinking Horizon Model Predictive Controller for Daily Scheduling of Home Energy Management Systems

In this paper, the model predictive control (MPC) strategy is utilized in smart homes to handle the optimal operation of controllable electrical loads of residential end-users. In the proposed model, active consumers reduce their daily electricity bills by installing photovoltaic (PV) panels and battery electrical energy storage (BEES) units. The optimal control strategy will be determined by the home energy management system (HEMS), benefiting from the meteorological and electricity market data stream during the operation horizon. In this case, the optimal scheduling of home appliances is managed using the shrinking horizon MPC (SH-MPC) and the main objective is to minimize the electricity cost. To this end, the HEMS is augmented by the SH-MPC, while maintaining the desired operation time slots of controllable loads for each day. The HEMS is cast as a standard mixed-integer linear programming (MILP) model that is incorporated into the SH-MPC framework. The functionality of the proposed method is investigated under different scenarios applied to a benchmark system while both time-of-use (TOU) and real-time pricing (RTP) mechanisms have been adopted in this study. The problem is solved using six case studies. In this regard, the impact of the TOU tariff was assessed in Scenarios 1&#x2013;3 while Scenarios 4&#x2013;6 evaluate the problem with the RTP mechanism. By adopting the TOU tariff and without any load shifting program, the cost is <inline-formula> <tex-math notation="LaTeX">$\$ $ </tex-math></inline-formula>1.2274 while by using the load shifting program without the PV and BEES system, the cost would reduce to <inline-formula> <tex-math notation="LaTeX">$\$ $ </tex-math></inline-formula>0.8709. Furthermore, by using the SH-MPC model, PV system and the BEES system, the cost would reduce to <inline-formula> <tex-math notation="LaTeX">$\$ $ </tex-math></inline-formula>-0.282713 with the TOU tariff. This issue shows that the prosumer would be able to make a profit. By adopting the RTP tariff and without any load shifting program, the cost would be <inline-formula> <tex-math notation="LaTeX">$\$ $ </tex-math></inline-formula>1.22093 without any PV and BEES systems. By using the SH-MPC model, the cost would reduce to <inline-formula> <tex-math notation="LaTeX">$\$ $ </tex-math></inline-formula>1.08383. Besides, by adopting the SH-MPC, and the PV and BEES systems, the cost would reduce to <inline-formula> <tex-math notation="LaTeX">$\$ $ </tex-math></inline-formula>0.05251 with the RTP tariff, showing the significant role of load shifting programs, local power generation, and storage systems.

Heuristics for Home Appliances Scheduling Problems with Energy Consumption Bounds

Heuristics for Home Appliances Scheduling Problems with Energy Consumption Bounds

A Review on Demand Side Management Applications, Techniques, and Potential Energy and Cost saving

Electrical peak load demand all over the world is always anticipated to grow, which is challenging electrical utility to supply such increasing load demand in a cost effective, reliable and sustainable manner. Thus, there is a need to study some of load management (LM) techniques employed to minimize energy consumption, reduce consumers' electricity bills and decrease the greenhouse gas emissions responsible for global warming. This paper presents a review of several recent LM strategies and optimization algorithms in different domains. The review is complemented by tabulating several demand side management (DSM) techniques with a specific view on the used demand response (DR) programs, key finding and benefits gained. A special focus is directed to the communication protocols and wireless technology, incorporation of renewable energy resources (RERs), battery energy storage (BES), home appliances scheduling and power quality applications. The outcome of this review reveals that the real time pricing (RTP) is the most efficient price-based mechanism program (PBP), whilst time of use (TOU) is the basic PBP and easiest to implement. Energy efficiency programs have proved the highest influential impact on the annual energy saving over the other dynamic pricing mechanism programs. Through a forecasted proposal of future study, DSM proved tremendous potential annual energy savings, peak demand savings, and investment cost rates within different consumption sectors progressively up to year 2030.

Fuzzy compromised solution-based novel home appliances scheduling and demand response with optimal dispatch of distributed energy resources

Due to environmental issues and smart grid development, distributed energy resources, energy storage systems, and demand response (DR) are gaining attention to reduce the pollution and fossil fuel usage. This paper presents a customer’s preferences based innovative home appliances scheduling framework considering numerous constraints and DR for scheduling household appliances incorporating local energy grid and energy storage systems, including electrical and thermal energy storage. First, the models of household appliances and air conditioning load are built as the shiftable and non-schedulable loads and a flexible thermal load, respectively. Second, an enhanced normalized normal constraint (ENNC) strategy based on game theory (GT) is presented for a novel home appliance scheduling (HAS) framework with the objectives to optimize consumption cost, end-users comfort, and peak to average ratio. Then, the fuzzy compromising (FCP) method is proposed to optimize overall energy cost and gaseous emissions for the novel HAS framework with a residential local energy grid. In addition to this, conditional value at risk (CVaR) has also been incorporated in the objective function to resolve the sudden absence of distributed energy resources and power failures. Finally, case studies on data from Dallas, Texas, USA are performed, and the simulation results show that the proposed strategy is computationally inexpensive and outperforms other approaches in terms of electricity cost, gaseous emissions, and customer’s comfort. The proposed approach gives a significantly lower cost of 104.30 cents and gaseous emissions of about 18.753 kg for an entire day of novel HAS with DR adoption. Thus, it can provide help for DR accomplishment and precise prediction of electricity consumption.

Home Energy Management for Community Microgrids Using Optimal Power Sharing Algorithm

From a residential point of view, home energy management (HEM) is an essential requirement in order to diminish peak demand and utility tariffs. The integration of renewable energy sources (RESs) together with battery energy storage systems (BESSs) and central battery storage system (CBSS) may promote energy and cost minimization. However, proper home appliance scheduling along with energy storage options is essential to significantly decrease the energy consumption profile and overall expenditure in real-time operation. This paper proposes a cost-effective HEM scheme in the microgrid framework to promote curtailing of energy usage and relevant utility tariff considering both energy storage and renewable sources integration. Usually, the household appliances have different runtime preferences and duration of operation based on user demand. This work considers a simulator designed in the C++ platform to address the domestic customer’s HEM issue based on usages priorities. The positive aspects of merging RESs, BESSs, and CBSSs with the proposed optimal power sharing algorithm (OPSA) are evaluated by considering three distinct case scenarios. Comprehensive analysis of each scenario considering the real-time scheduling of home appliances is conducted to substantiate the efficacy of the outlined energy and cost mitigation schemes. The results obtained demonstrate the effectiveness of the proposed algorithm to enable energy and cost savings up to 37.5% and 45% in comparison to the prevailing methodology.

Smart Home Battery for the Multi-Objective Power Scheduling Problem in a Smart Home Using Grey Wolf Optimizer

The power scheduling problem in a smart home (PSPSH) refers to the timely scheduling operations of smart home appliances under a set of restrictions and a dynamic pricing scheme(s) produced by a power supplier company (PSC). The primary objectives of PSPSH are: (I) minimizing the cost of the power consumed by home appliances, which refers to electricity bills, (II) balance the power consumed during a time horizon, particularly at peak periods, which is known as the peak-to-average ratio, and (III) maximizing the satisfaction level of users. Several approaches have been proposed to address PSPSH optimally, including optimization and non-optimization based approaches. However, the set of restrictions inhibit the approach used to obtain the optimal solutions. In this paper, a new formulation for smart home battery (SHB) is proposed for PSPSH that reduces the effect of restrictions in obtaining the optimal/near-optimal solutions. SHB can enhance the scheduling of smart home appliances by storing power at unsuitable periods and use the stored power at suitable periods for PSPSH objectives. PSPSH is formulated as a multi-objective optimization problem to achieve all objectives simultaneously. A robust swarm-based optimization algorithm inspired by the grey wolf lifestyle called grey wolf optimizer (GWO) is adapted to address PSPSH. GWO has powerful operations managed by its dynamic parameters that maintain exploration and exploitation behavior in search space. Seven scenarios of power consumption and dynamic pricing schemes are considered in the simulation results to evaluate the proposed multi-objective PSPSH using SHB (BMO-PSPSH) approach. The proposed BMO-PSPSH approach’s performance is compared with that of other 17 state-of-the-art algorithms using their recommended datasets and four algorithms using the proposed datasets. The proposed BMO-PSPSH approach exhibits and yields better performance than the other compared algorithms in almost all scenarios.

Home Appliances in the Smart Grid: A Heuristic Algorithm-Based Dynamic Scheduling Model

Customers and power utilities alike will benefit from smart grid technology by lowering energy prices and regulating generating capability. The accuracy of information sharing between main grids and smart meters is critical to the performance of scheduling algorithms. Customers, on the other hand, are expected to plan loads, respond to electricity demand alerts, engage in energy bidding, and constantly track the utility company's energy rates. Consumer loyalty can be improved by strengthening the connectivity infrastructure between the service provider and its customers. We suggest a heuristic demand-side control model for automating the scheduling of smart home appliances in order to optimize the comfort of the customers involved. Simulation findings show that the suggested hybrid solution will reduce the peak-to-average ratio of overall energy demand while still lowering total energy costs without sacrificing consumer convenience

Smart home appliances scheduling considering user comfort level

&lt;a name="_Hlk38174666"&gt;&lt;/a&gt;&lt;span&gt;Smart home appliances scheduling, employing optimization optimization algorithms to reduce utility costs, is gaining traction under the introduction of time-of-use tariffs and the development of Internet of Things (IoT). The prior electricity cost reduction scheduling algorithms, however, causes substantial discomfort to users for restricting users from using the appliances at their desired times. To address the problem, a novel versatile systematic method is proposed by pricing the mismatch of proposed schedule with users’ usage preference pattern to quantify discomfort, coupled with comfort-cost weight factor. The method employing customizable user preference patterns, user-perceived pricing of mismatch and user-specified comfort-savings weightage, not only captures the complex dependence of comfort to individual preference, but the evolution with time by continuous user survey. The proposed method, formulated to be simple enough to be applied on an Excel spreadsheet, demonstrates substantial reduction of electricity cost and users’ discomfort simultaneously. Studies on the algorithm found it to be robust against of fluctuations of parameters, with optimization performance comparable to prior work. The work demonstrates that despite the complex nature of comfort to users’ behaviors and perception, simple pricing surveys can be used to accurately quantify, compare and optimize users’ comfort together with economic savings. &lt;/span&gt;