Integrating User Preference Into Improved Home Appliance Scheduling

2021 ◽  
Author(s):  
Jacob Starks ◽  
Li Song ◽  
Janet K. Allen ◽  
Farrokh Mistree

Abstract Primary Question – How can smart appliance networks integrate human preference to enhance appliance scheduling? To deal with user preference variability, where the physical network interacts with human behavior, the most effective method is a flexible Graphical User Interface (GUI), or dashboard. In this work a dashboard is developed to make a more flexible model, this dashboard can account for variability in load preference, goal preference and appliance specifications, allowing consumers to plan loads on their specific network of household appliances in order to schedule a preferred time and evaluate the costs of certain load timing, given the desire to minimize the cost of electricity, avoid exceeding a peak load with minimal deviations from the user preferred schedule. As a result, uncertainty due to users is mitigated, such that only uncertainty in the load cycles themselves had to be managed, and that management could be done with greater robustness and computational efficiency. Consequently, this provides a model for developing more computationally efficient and robust scheduling patterns for household appliances. In this paper, household appliances are treated as an interdependent network to find satisficing solutions for timing loads to minimize electric cost, peak load, and deviation from the preferred time of scheduling.

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2304 ◽  
Author(s):  
Mingfu Li ◽  
Guan-Yi Li ◽  
Hou-Ren Chen ◽  
Cheng-Wei Jiang

To reduce the peak load and electricity bill while preserving the user comfort, a quality of experience (QoE)-aware smart appliance control algorithm for the smart home energy management system (sHEMS) with renewable energy sources (RES) and electric vehicles (EV) was proposed. The proposed algorithm decreases the peak load and electricity bill by deferring starting times of delay-tolerant appliances from peak to off-peak hours, controlling the temperature setting of heating, ventilation, and air conditioning (HVAC), and properly scheduling the discharging and charging periods of an EV. In this paper, the user comfort is evaluated by means of QoE functions. To preserve the user’s QoE, the delay of the starting time of a home appliance and the temperature setting of HVAC are constrained by a QoE threshold. Additionally, to solve the trade-off problem between the peak load/electricity bill reduction and user’s QoE, a fuzzy logic controller for dynamically adjusting the QoE threshold to optimize the user’s QoE was also designed. Simulation results demonstrate that the proposed smart appliance control algorithm with a fuzzy-controlled QoE threshold significantly reduces the peak load and electricity bill while optimally preserving the user’s QoE. Compared with the baseline case, the proposed scheme reduces the electricity bill by 65% under the scenario with RES and EV. Additionally, compared with the method of optimal scheduling of appliances in the literature, the proposed scheme achieves much better peak load reduction performance and user’s QoE.


2013 ◽  
Vol 772 ◽  
pp. 705-710
Author(s):  
Li Wei Ju ◽  
Zhong Fu Tan ◽  
He Yin ◽  
Zhi Hong Chen

In order to be able to absorb the abandoned wind, increasing wind-connect amount, the paper study the way of wind power, thermal power joint run and puts forward wind power, thermal power joint run optimization model based on the energy-saving generation dispatching way under the environment of TOU price and the target of minimizing the cost of coal-fired cost, unit commitment and pollution emissions. The numerical example finds, the TOU price can realize the goal of peak load shifting, increasing the electricity demand in the low load and reducing electricity demand in the peak load. The model can increase the amount of wind-connect grid, absorb the abandoned wind, reduce the use of coal-fired units under the environment, increase the average electricity sales price and profit of Power Company. Therefore, the model has significant economical environmental benefits


2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xuelian Yang ◽  
Jin Bai ◽  
Xiaolin Wang

With the development of Internet technology and social model, game products have become an important product of people’s life for entertainment and recreation, and the precise marketing of game products has become a winning means for enterprises to improve competitiveness and reduce labor cost consumption, and major game companies are also paying more and more attention to the data-based marketing model. How to dig out the effective information from the existing market behavior data is a powerful means to implement precise marketing. Achieving precise positioning and marketing of gaming market is the guarantee of innovative development of game companies. For the research on the above problem, based on the SEMAS process of data mining, this paper proposes a mining model based on recurrent neural network, which is named as Dynamic Attention GRU (DAGRU) with multiple dynamic attention mechanisms, and evaluates it on two self-built data sets of user behavior samples. The results demonstrate that the mining method can effectively analyze and predict the player behavior goals. The game marketing system based on data mining can indeed provide more accurate and automated marketing services, which greatly reduces the cost investment under the traditional marketing model and achieves accurate targeting marketing services and has certain application value.


Energies ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 4033 ◽  
Author(s):  
Moon ◽  
Kim ◽  
Nam

Geothermal energy has attracted attention as a high-efficiency energy source that can be used year-round, but it has a relatively higher initial investment cost. For the design of ground source heat pump (GSHP) systems, a calculation method to determine the capacity of a system to meet the peak load of the target building is usually used. However, this method requires excessive system capacity design, especially regarding buildings with partial load operations. In this study, the optimization of a system design was performed in the view of the cost of the lifecycle cost. Several optimization algorithms were considered, such as the discrete Armijo gradient algorithm, a particle swarm optimization (PSO) algorithm, and a coordinate search method algorithm. The results of the optimization described the system capacity (heat pump, ground heat exchanger, thermal storage tank, etc.) and the cost performance, showing that the total investment cost was reduced compared to the existing design.


2013 ◽  
Vol 805-806 ◽  
pp. 452-457
Author(s):  
Wen Bo Mao ◽  
Ke Wang ◽  
Jian Tao Liu

A model of continuous optimized power flow (COPF) is proposed, concluding demand response (DR). According to different implementation mechanisms, a series of DR models are built, such as: time of use (TOU), real time price (RTP), critical peak price (CPP), and interruptible load (IL). The influences of these kinds of DR on power system are analyzed, including peak load reduction, cost reduction, and reservation optimization. The results show that: DR can cut the cost, reduce the peak load, and promote the reservation optimization.


2015 ◽  
Vol 30 (5) ◽  
pp. 1140-1157 ◽  
Author(s):  
Qin Xu ◽  
Li Wei ◽  
Kang Nai

Abstract A computationally efficient method is developed to analyze the vortex wind fields of radar-observed mesocyclones. The method has the following features. (i) The analysis is performed in a nested domain over the mesocyclone area on a selected tilt of radar low-elevation scan. (ii) The background error correlation function is formulated with a desired vortex-flow dependence in the cylindrical coordinates cocentered with the mesocyclone. (iii) The square root of the background error covariance matrix is derived analytically to precondition the cost function and thus enhance the computational efficiency. Using this method, the vortex wind analysis can be performed efficiently either in a stand-alone fashion or as an additional step of targeted finescale analysis in the existing radar wind analysis system developed for nowcast applications. The effectiveness and performance of the method are demonstrated by examples of analyzed wind fields for the tornadic mesocyclones observed by operational Doppler radars in Oklahoma on 24 May 2011 and 20 May 2013.


Author(s):  
Bethany Pickett ◽  
Cameron J. Turner ◽  
Anthony Petrella

Probabilistic simulation methods have allowed for many advancements in the field of biomechanics, especially for the human spine. To accurately model a complex system such as the spine, the model must account for the differences that occur from one specimen to the next. These differences in material properties and anatomical shapes are described probabilistically. Accurately modeling the effects of these differences is important in biomechanics as no two people are exactly alike, yet building individual models of every person is impractical. Several authors have conducted research into more accurate ways to model biomechanical systems such as the spine, however the computational expense of performing analysis and optimization with these probabilistic simulation models still remains an issue, particularly with respect to the underlying Monte Carlo simulations. The research described in this paper investigates the use of Non-Uniform Rational B-splines (NURBs) based metamodels to reduce the cost of expensive probabilistic simulation models of the spine for analysis and optimization. Metamodels are simply mathematical approximations of a model or in other words, a model of models. Metamodels are widely used to represent the behavior of complex systems based on limited data from the original system model. Metamodels are often more computationally efficient to store and analyze than the original system models which they approximate. Using a Functional Spinal Unit (FSU) Finite Element Model, two different probabilistic NURBs-based metamodeling methods were developed and tested. Through the use of metamodels, a promising approach for reducing the computational time of running a Monte Carlo simulation was discovered.


2014 ◽  
Vol 622-623 ◽  
pp. 165-173 ◽  
Author(s):  
Nicholas J. Politis ◽  
Denis J. Politis ◽  
Catrin Mair Davies ◽  
Jian Guo Lin ◽  
Trevor A. Dean

A significant factor in the cost of industrial machinery for precision forging is the maximum load required to fully forge the final shape of components. Typically in a precision forging process, the required load increases greatly towards the end of the stroke. This study focuses on reducing the final sharp increase in load encountered in a typical closed die forging setup. A technique of reducing the peak load in the forging of gears is proposed, named the Peripheral Relief (PR) method. A gear forging tool set has been designed and manufactured. A number of experimental trials have been performed using model materials to investigate the force reduction technique. An efficient and simplified FE model has been developed to evaluate the effects of the PR method. The experimental load characteristics are compared to the simulated results. The method has been found, both numerically and experimentally, to significantly reduce the peak load encountered at the end of the forging stroke compared to current closed die forging techniques.


2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Mirelys Torres Pérez ◽  
Javier Domínguez Bravo ◽  
Cesar Hernández Leyva ◽  
Marieta Peña Abreu

Energy access is a crucial step for the socio-economic development of isolated communities and for preventing disease and fight pandemics across many parts of the world. In this research, is presented a freeware GIS tool for the techno-economic evaluation of rural electrification alternatives, based on a plugin for the QGIS (Quantum GIS) called LECGIS. The tool carries out an implementation of the IntiGIS model to perform the calculations, a flexible model capable of adapting to the realities of different scenarios. In addition, it allows the clustering of isolated houses, for a better modelling of the cost of the centralized systems. It is described the application of this tool in the Guamá (Cuba) case study and the comparisons of the results with the obtained in Intigis 1.0. It is concluded that the LECGIS plugin allows to calculate and compare seven technological options for the electrification of communities, supporting the decision-making in the planning of rural electrification projects.


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