Model predictive control of heat pump water heater-instantaneous shower powered with integrated renewable-grid energy systems

Wanjiru, Evan M.; Sichilalu, Sam M.; Xia, Xiaohua

doi:10.1016/j.apenergy.2017.05.033

Cited by 55 publications

(21 citation statements)

References 65 publications

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“…The traditional method was to predict the aggregated hot water demand directly. This was also achieved using SVM (37) for regression in this study. All parameters in the SVM had been cross validated to find the best combination.…”

Section: Comparative Prediction Performancementioning

confidence: 82%

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Predictive control based on occupant behavior prediction for domestic hot water system using data mining algorithm

Cao

Hou

et al. 2019

Energy Science & Engineering

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Domestic hot water systems are a primary source of energy consumption in buildings, and present a promising future in terms of energy saving. Predictive control methods have been used to reduce energy consumption during an operation period. However, current methods lack consideration of occupant behavior, which significantly influences the prediction results. In this study, a data‐based predictive method is proposed to predict the shower behavior of occupants. A dataset was collected from seven occupants and was trained using support vector machine (SVM) to learn their showering habits. These results were used to predict the hot water demand. A comparative analysis shows that, in this way, the prediction results are more reliable under predictive control than predicting the hot water demand directly using SVM. A simulation of a domestic hot water system was then conducted. The results indicate that the proposed approach can reduce the heat loss from a hot water storage tank (ST) by up to 33% compared to a traditional control method, while maintaining a high assurance of the hot water supply (an insufficient hot water supply of less than 1%) with little change to the original system.

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Section: Comparative Prediction Performancementioning

confidence: 82%

“…35 Among which, Matlab has its advantages for convenience of coding and a variety of built-in functions. 37 LIBSVM 3.22 was used to build the predictive model and the model of DHW system was programmed in Matlab R2017b. The flowchart of simulation process is shown in Figure 9.…”

Section: Simulation Processmentioning

confidence: 99%

Predictive control based on occupant behavior prediction for domestic hot water system using data mining algorithm

Cao

Hou

et al. 2019

Energy Science & Engineering

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“…This research designs a control strategy that integrates the MPC controller to promote economic savings. In [32], a robust and economical closed-loop MPC approach that can handle the energy flow of a hybrid system is investigated. This strategy shows that the MPC is robust and reliable to deal with the overall system disturbances.…”

Section: Mpc-based Energy Managementmentioning

confidence: 99%

“…The development of constraints that a given system can have has to be less or equal to the total number of constraints which are defined in the MPC system design. Equation (32) describes the number of constraints that an MPC system design can handle [53].…”

Section: System Constraintsmentioning

confidence: 99%

An optimal energy management system for a commercial building with renewable energy generation under real-time electricity prices

Mbungu

Bansal

Naidoo

et al. 2018

Sustainable Cities and Society

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Highlights• Develops a real-time electricity pricing structure in conjunction with TOU electricity tariff. • Develops an adaptive strategy in the framework of the smart grid using MPC design. • Develops a new approach to optimization of the cost of energy in real-time electricity pricing environment. • Designs an optimal strategy for integrating PV with battery storage to the grid in realtime electricity pricing scheme. • Gives the consumer an opportunity to deal with the cost of electricity usage.Abstract-This paper presents an approach to the energy management and control of the effective cost of energy in real-time electricity pricing environment. The strategy aims to optimise the overall energy flow in the electrical system that minimises the cost of power consumption from the grid. To substantiate these claims different cases of time-of-use (TOU) and renewable energy electricity tariff, i.e. in summer and winter seasons, and the robustness of system is analysed. A given energy demand for commercial usage in the city of Tshwane (South Africa) is used to investigate the behaviour of the designed method during low and high demand periods. As grid integrated renewable energy resources, photovoltaic (PV) is an important consideration in assuring excellent power supply and environmental issues in the commercial building. An adaptive optimal approach in the framework of model predictive control (MPC) is designed to coordinate the energy flow on the electrical system. The results show that the proposed adaptive MPC strategy can promote the new approach of an optimal electrical system design, which reduces the energy cost to pay the utility grid by about 46 % or more depending on the set target. Index Terms-Battery bank, Energy management, Model predictive control, Photovoltaic, Smart grid, Time-of-use tariff. NOMENCLATURE state matrix (-) input matrix (-) cost of electricity to pay or output vector (Rand or R) 1 cost of electricity to pay the utility grid (Rand or R) 2 cost of electricity consumption from the renewable energy (Rand or R) 3 cost of electricity consumption by the load (Rand or R) output matrix (-) 1 input vector (kWh) 1 energy flow of the utility grid (kWh) 2 energy flow of the PV (kWh) 3 energy flow of the charging of battery (kWh) 4 energy flow of the discharging of battery (kWh) energy consumption (kWh) charging energy on battery (kWh) discharging energy from battery (kWh) The deployment of buildings' energy management systems started in the 1970s when the development of direct digital control signal from the advent of a microprocessor was introduced.It offers the ability to control and manage the energy system by providing the users with a betterquality environment. Since its inception, it has been observed that the development of energy Page 3 of 38 energy demand (kWh) energy drawn from battery (kWh) energy consumption at sample (kWh) energy supply from grid (kWh) maximum energy from battery (kWh) energy nominal on battery (kWh) energy supply from photovoltaic array (kWh) PV energy consumption at...

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“…Sichilalu et al [3] focused on HP water heaters to reduce energy cost based on time-of-use (TOU) tariff, by presenting an optimal scheduling model. Wanjiru et al [4] presented an optimal model to minimize energy and water consumptions. The authors controlled an HP water heater and an instant heater which are integrated with a wind turbine, PV system, and diesel generator.…”

Section: Introductionmentioning

confidence: 99%

Demonstration of Peak-Load Shifting with Optimal Residential Thermal Energy Management

Baniasadi¹,

Habibi²,

Al-Saedi³

et al. 2019

EasyChair Preprints

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The high penetration of renewable energy resources (RES), in particular the rooftop photovoltaic (PV) systems in power systems, causes rapid ramps in power generation to supply load during peak-load periods. In smart residential buildings, variations in rooftop PV power causes a mismatch between generation and load demand. This paper deals with shifting heat pumps loads to either the lower electricity price period or whenever PV generation is available. A strategy is proposed for managing heat pump operation based on real-time pricing tariff to minimize the operation cost of a smart building by controlling the room temperature. Simulation results demonstrate the cost benefits and effectiveness of the proposed thermal energy management strategy.

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Model predictive control of heat pump water heater-instantaneous shower powered with integrated renewable-grid energy systems

Cited by 55 publications

References 65 publications

Predictive control based on occupant behavior prediction for domestic hot water system using data mining algorithm

Predictive control based on occupant behavior prediction for domestic hot water system using data mining algorithm

An optimal energy management system for a commercial building with renewable energy generation under real-time electricity prices

Demonstration of Peak-Load Shifting with Optimal Residential Thermal Energy Management

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