A biased load manager home energy management system for low-cost residential building low-income occupants

Monyei, Chukwuka G.; Adediran, Adewumi; Akinyele, Daniel; Babatunde, Olubayo Moses; Obolo, Michael O.; Onunwor, Joshua C.

doi:10.1016/j.energy.2018.03.016

Cited by 17 publications

(6 citation statements)

References 56 publications

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“…In addition to optimal appliance scheduling and user comfort, the works proposed by Monyei et al (2018), Shakeri et al (2018), and Shafie-Khah and Siano (2018) also consider the usage of renewable sources of energy like photovoltaic and wind power generation, as well as energy storage devices. To model user comfort, they take into consideration several factors, such as room temperature levels and usage of storage, instead of shifting.…”

Section: Home Energy Management Systemsmentioning

confidence: 99%

Household appliance usage recommendation based on demand forecasting and multiobjective optimization

Feitosa

Lacerda

Santos

et al. 2022

RSD

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Accelerated population growth in the 21st century and increased demand for energy sources, associated with climate change, have resulted in two main challenges: the search for sustainable energy sources and the need to find more efficient ways to use extant sustainable sources. The forecasting module provides an estimate of the future usage of these appliances and it is the source of the recommended module’s suggestion. Time Series Forecasting techniques, such as Autoregressive Integrated Moving Average, LongShort Term Memory (LSTM), Gated Recurrent Units, Echo State Networks (ESN), and Support Vector Regression, were tested for the predictive module. Multiobjective optimization techniques such as NonSorted Genetic Algorithm II (NSGA II), MultiObjective Particle Swarm Optimization (MOPSO), Speed constrained Multi-objective Particle Swarm Optimization (SMOPSO), and Strength Pareto Evolutionary Algorithm two (SPEA2), for example, were tested for the Recommendation Module. The Forecasting and Recommendation module experiments were performed independently. In the Forecasting Module, the results and statistical tests revealed LSTM as the best suited technique for forecasting the loads of the majority of the appliances tested (in this case seven) in terms of root mean square error. In the experiments performed for the recommendation module, NSGA II showed a higher overall performance compared to other metrics in terms of hyper volume of the Pareto Front generated. This work presents the potential of using both Predictive Models and MultiObjective Optimization Techniques combined to reduce energy usage in household environments.

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Section: Home Energy Management Systemsmentioning

confidence: 99%

Household appliance usage recommendation based on demand forecasting and multiobjective optimization

Feitosa

Lacerda

Santos

et al. 2022

RSD

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“…Values of some parameters in the IES 18,[24][25][26] Parameter Value Parameter Value water specific heat capacity (kJ/[kg C]), η ST is the thermal efficiency and A ST is the area (m 2 ).…”

Section: Gridmentioning

confidence: 99%

Stochastic model predictive control operation strategy of integrated energy system based on temperature‐flowrate scheduling model considering detailed thermal characteristics

Wei

Sun

et al. 2020

Int J Energy Res

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Summary A scheduling model is a prerequisite for an operation strategy of integrated energy system (IES). Existing scheduling models of IES, however, are typically based on heat‐transfer variables either completely or partially, which oversimplify detailed thermal characteristics. To this end, a novel scheduling model is proposed where all thermal processes are modeled by temperature and flowrate of working fluids. This improvement renders the capability to the scheduling model to incorporate different thermal processes. Furthermore, the nonlinear product terms of temperature and flowrate in the proposed model are linearized by the binary expansion method. Based on the linearized scheduling model, a stochastic model predictive control (SMPC) operation strategy is exploited to optimize the economic performance by energy forecast, scenario reduction, rolling optimization, and feedback correction. Afterwards, four operation modes considering different temperature changes of the devices, networks, and the environment are performed and compared. The results found that thermal characteristics will affect device operation results and the degree of influence varies. The network temperature changes have the broadest influence, followed by the device and the ambient temperature changes. Moreover, system operation costs are also affected by detailed thermal characteristics. The total cost, the gas cost, and the electricity cost under Mode 2 are almost the same to those of Mode 1. However, the first two costs are reduced by 3.4% and 5.3% under Mode 3, and are reduced by 2.7% and 4% under Mode 4, despite that the electricity cost increases by 0.2% under Mode 3 and remains almost the same under Mode 4. These indicate that reliability and economy of an IES are affected by thermal characteristics, and it is thus the necessity to consider detailed thermal characteristics in an operation. Moreover, the results demonstrate the capability of the generalized temperature‐flowrate based scheduling model and the effectiveness of the SMPC operation strategy.

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“…Constraint (40) ensures null values for the intervals when the EV battery cannot be used. Constraint (41) calculates the fuel cost for each trip s. Constraint (42) gives the total cost of the fuel consumption. Constraint (43) is the binary and nonnegative restrictions.…”

Section: Plug-in Hybrid Ev Modelmentioning

confidence: 99%

A realistic energy optimization model for smart‐home appliances

2019

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Summary Smart homes have the potential to achieve optimal energy consumption with appropriate scheduling. The control of smart appliances can be based on optimization models, which should be realistic and efficient. However, increased realism also implies an increase in solution time. Many of the optimization models in the literature have limitations on the types of appliances considered and/or their reliability. This paper proposes a home energy management scheduling model that is more realistic and efficient. We develop a mixed integer linear optimization model that minimizes the energy cost while maintaining a given level of user comfort. Our main contribution is the variety of specific appliance models considered and their integration into a single model. We consider the use of energy in appliances and electric vehicles (EVs) and take into account renewable local generation, batteries, and demand response. Our models of a shower, a fridge, and a hybrid EV consider both the electricity consumption and the conventional fuel cost. We present computational results to validate the model and indicate how it overcomes the limitations of other models. Our results, compared with the best competitors, provide cost savings ranging from 8% to 389% over a horizon of 24 hours.

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A biased load manager home energy management system for low-cost residential building low-income occupants

Cited by 17 publications

References 56 publications

Household appliance usage recommendation based on demand forecasting and multiobjective optimization

Household appliance usage recommendation based on demand forecasting and multiobjective optimization

Stochastic model predictive control operation strategy of integrated energy system based on temperature‐flowrate scheduling model considering detailed thermal characteristics

A realistic energy optimization model for smart‐home appliances

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A biased load manager home energy management system for low-cost residential building low-income occupants

Cited by 17 publications

References 56 publications

Household appliance usage recommendation based on demand forecasting and multi­objective optimization

Household appliance usage recommendation based on demand forecasting and multi­objective optimization

Stochastic model predictive control operation strategy of integrated energy system based on temperature‐flowrate scheduling model considering detailed thermal characteristics

A realistic energy optimization model for smart‐home appliances

Contact Info

Product

Resources

About

Household appliance usage recommendation based on demand forecasting and multiobjective optimization

Household appliance usage recommendation based on demand forecasting and multiobjective optimization