Energy and HVAC: Sensors in HVAC Systems for Metering and Energy Cost Allocation

2008 ◽  
pp. 159-172
Author(s):  
Gnter Mgge
Keyword(s):  
Energy Cost ◽  
Cost Allocation ◽  
Hvac Systems

scholarly journals Operational optimization of residential HVAC system using model predictive control strategy planning

2021 ◽  
Author(s):  
Nima Alibabaei ◽  
Alan S. Fung
Keyword(s):  
Cost Saving ◽  
Energy Cost ◽  
Carbon Dioxide Emissions ◽  
Hvac Systems ◽  
Conservation Strategy ◽  
Hvac System ◽  
Heating Season ◽  
Residential Sector ◽  
Simulation Engine ◽  
Strategy Planning

To date, the residential sector accounts for a major portion of consumption by consuming more than 40% of the entire world's energy and producing 33% of the carbon dioxide emissions. In North America, the residential sector energy consumptions are mainly related to heating, ventilation, and air conditioning (HVAC) systems, which are not operating in the most efficient ways due to existing on/off and conventional controllers. In Ontario, due to the variable price of electricity, variation in outdoor disturbances, and new Ontario Government sweeping mandate in overhauling the energy use in residential sector, there is an opportunity to develop intelligent control systems to employ energy conservation strategy planning model (ECSPM) in existing HVAC systems for reducing their operating cost, energy consumption, and GHG emission. In order to take advantage of these opportunities, two model-based predictive controllers (MPCs) were developed in this Ph.D. research. In the first MPC controller, a Matlab-TRNSYS co-simulator was developed to fill the lack of advanced controllers in building energy simulators. This cosimulator investigated the effectiveness of different novel ECSPMs on an HVAC system's energy cost saving during winter and summer seasons. This co-simulator offered 23.8% saving in the HVAC system's energy costs in the heating season. Regardless of the strong capabilities, employing this co-simulator for implementing comprehensive/complex optimization methods resulted in an unacceptably long optimization time due to the of TRNSYS simulation engine. Therefore, in the second PMC controller, simplified house thermal and HVAC system models were developed in Matlab. To design a grid-friendly house, this model was enhanced by integrating on-site renewable energy generation and storage systems. A novel algorithm was developed to reduce the MPC controller optimization time. The effectiveness of the novel MPC model in the HVAC system's energy cost saving was compared with a Simple Rule-based (SRB) controller, which itself is an efficient HVAC controller, while this controller offered 12.28% additional savings in the heating season.

scholarly journals Operational optimization of residential HVAC system using model predictive control strategy planning

2021 ◽  
Author(s):  
Nima Alibabaei ◽  
Alan S. Fung
Keyword(s):  
Cost Saving ◽  
Energy Cost ◽  
Carbon Dioxide Emissions ◽  
Hvac Systems ◽  
Conservation Strategy ◽  
Hvac System ◽  
Heating Season ◽  
Residential Sector ◽  
Simulation Engine ◽  
Strategy Planning

To date, the residential sector accounts for a major portion of consumption by consuming more than 40% of the entire world's energy and producing 33% of the carbon dioxide emissions. In North America, the residential sector energy consumptions are mainly related to heating, ventilation, and air conditioning (HVAC) systems, which are not operating in the most efficient ways due to existing on/off and conventional controllers. In Ontario, due to the variable price of electricity, variation in outdoor disturbances, and new Ontario Government sweeping mandate in overhauling the energy use in residential sector, there is an opportunity to develop intelligent control systems to employ energy conservation strategy planning model (ECSPM) in existing HVAC systems for reducing their operating cost, energy consumption, and GHG emission. In order to take advantage of these opportunities, two model-based predictive controllers (MPCs) were developed in this Ph.D. research. In the first MPC controller, a Matlab-TRNSYS co-simulator was developed to fill the lack of advanced controllers in building energy simulators. This cosimulator investigated the effectiveness of different novel ECSPMs on an HVAC system's energy cost saving during winter and summer seasons. This co-simulator offered 23.8% saving in the HVAC system's energy costs in the heating season. Regardless of the strong capabilities, employing this co-simulator for implementing comprehensive/complex optimization methods resulted in an unacceptably long optimization time due to the of TRNSYS simulation engine. Therefore, in the second PMC controller, simplified house thermal and HVAC system models were developed in Matlab. To design a grid-friendly house, this model was enhanced by integrating on-site renewable energy generation and storage systems. A novel algorithm was developed to reduce the MPC controller optimization time. The effectiveness of the novel MPC model in the HVAC system's energy cost saving was compared with a Simple Rule-based (SRB) controller, which itself is an efficient HVAC controller, while this controller offered 12.28% additional savings in the heating season.

scholarly journals Automated Control of Transactive HVACs in Energy Distribution Systems

2020 ◽  
Author(s):  
Boming Liu ◽  
Akcakaya Murat ◽  
Tom McDermott
Keyword(s):  
Energy Cost ◽  
Distribution Systems ◽  
Learning Algorithm ◽  
Hvac Systems ◽  
Comfort Level ◽  
Automated Control ◽  
Electricity Cost ◽  
Markov Decision ◽  
History Of ◽  
Residential Demand

<div>Heating, Ventilation, and Air Conditioning (HVAC) systems contribute significantly to a building’s energy consumption.</div><div>In the recent years, there is an increased interest in developing transactive approaches which could enable automated and flexible scheduling of HVAC systems based on the customer demand and the electricity prices decided by the suppliers. Flexible and automated scheduling of the HVAC systems make it a prime source for participation in residential demand response or transactive energy systems. Therefore, it is of significant interest to identify an optimal strategy to control the HVAC systems. In this paper, reducing the energy cost while keeping the comfort level acceptable to the users, we argue that such a control strategy should consider both the energy cost and user c omfort simultaneously. Accordingly, we develop the control</div><div>strategy through the solution of an optimization problem that balances between the energy cost and consumer’s dissatisfaction. This optimization enables us to solve a decision-making problem through first price prediction and then choosing HVAC temperature settings throughout the day based on the predicted price, history of the price and HVAC settings, and outside temperature. More specifically, we formulate the control design as a Markov decision process (MDP) using deep neural networks and use Deep Deterministic Policy Gradients (DDPG)-based deep reinforcement learning algorithm to find the optimal control</div><div>strategy for HVAC systems that balances between electricity cost and user comfort.</div>

scholarly journals Cost Control Methods for Efficient HVAC in Office Building

2019 ◽  
Vol 9 (2S2) ◽  
pp. 511-515
Keyword(s):  
Energy Efficiency ◽  
Indoor Air ◽  
Energy Cost ◽  
Sustainable Design ◽  
Hvac Systems ◽  
Building Envelope ◽  
Office Buildings ◽  
Office Building ◽  
Control Methods ◽  
Wall Construction

Achieving Energy Efficiency in Office Buildings plays a key role in reducing the Environmental Impact of Buildings to a larger extent. The Users in the workplace are often affected by the improper design of HVAC systems. In most of the office buildings the Indoor Environmental conditions were not designed, controlled and maintained which in turn increases the Energy cost of the buildings. Sustainable Design of HVAC Systems includes all the mechanical equipments that efficiently controls, monitors and supplies the Indoor Air. The objective of this paper is to (i) Do a comparative study and analyses the various building Envelope in office buildings for reducing the Energy Cost in designing HVAC systems in Office buildings using Ecotect Modelling.(ii) To compare the Energy cost of Water Cooled Screw Chillers and VRF Systems. The above experimentation was held in ELCOT S office building in salem. The findings of this paper revealed that usage of Porotherm wall construction along with VRF SYSTEMS in office buildings found to be effective in achieving sustainable HVAC design.

Energy-cost allocation based on the theory of frequency response

2004 ◽  
Vol 79 (4) ◽  
pp. 371-383 ◽  
Author(s):  
Ye Yao ◽  
Zhiwei Lian ◽  
Shiqing Liu ◽  
Zhijian Hou
Keyword(s):  
Frequency Response ◽  
Energy Cost ◽  
Cost Allocation

scholarly journals Automated Control of Transactive HVACs in Energy Distribution Systems

2020 ◽  
Author(s):  
Boming Liu ◽  
Akcakaya Murat ◽  
Tom McDermott
Keyword(s):  
Energy Cost ◽  
Distribution Systems ◽  
Learning Algorithm ◽  
Hvac Systems ◽  
Comfort Level ◽  
Automated Control ◽  
Electricity Cost ◽  
Markov Decision ◽  
History Of ◽  
Residential Demand

<div>Heating, Ventilation, and Air Conditioning (HVAC) systems contribute significantly to a building’s energy consumption.</div><div>In the recent years, there is an increased interest in developing transactive approaches which could enable automated and flexible scheduling of HVAC systems based on the customer demand and the electricity prices decided by the suppliers. Flexible and automated scheduling of the HVAC systems make it a prime source for participation in residential demand response or transactive energy systems. Therefore, it is of significant interest to identify an optimal strategy to control the HVAC systems. In this paper, reducing the energy cost while keeping the comfort level acceptable to the users, we argue that such a control strategy should consider both the energy cost and user c omfort simultaneously. Accordingly, we develop the control</div><div>strategy through the solution of an optimization problem that balances between the energy cost and consumer’s dissatisfaction. This optimization enables us to solve a decision-making problem through first price prediction and then choosing HVAC temperature settings throughout the day based on the predicted price, history of the price and HVAC settings, and outside temperature. More specifically, we formulate the control design as a Markov decision process (MDP) using deep neural networks and use Deep Deterministic Policy Gradients (DDPG)-based deep reinforcement learning algorithm to find the optimal control</div><div>strategy for HVAC systems that balances between electricity cost and user comfort.</div>

scholarly journals Distributed Energy Trading Management for Renewable Prosumers with HVAC and Energy Storage

2021 ◽  
Vol 20 ◽  
pp. 97-110
Author(s):  
Qing Yang ◽  
Hao Wang
Keyword(s):  
Energy Management ◽  
Private Information ◽  
Energy Cost ◽  
Peak Load ◽  
Cost Effective ◽  
Energy System ◽  
Hvac Systems ◽  
Real World Data ◽  
Energy Trading ◽  
Improve Energy Efficiency

Heating, ventilating, and air-conditioning (HVAC) systems consume a large amount of energy in residential houses and buildings. Effective energy management of HVAC is a cost-effective way to improve energy efficiency and reduce the energy cost of residential users. This work develops a novel distributed method for the residential transactive energy system that enables multiple users to interactively optimize their energy management of HVAC systems and behind-the-meter batteries. Specifically, this method effectively reduces the cost of smart homes by employing energy trading among users to leverage their power usage flexibility without compromising the users’ privacy. To achieve this goal, we design a distributed optimization algorithm based on the alternating direction method of multipliers (ADMM) to automatically operate the HVAC system and batteries, which minimizes the energy costs of users. Specifically, we decouple the optimization problem into a primal subproblem and a dual subproblem. The primal subproblem is solved by the users, and the dual subproblem is solved by the grid operator. Unlike the existing centralized method, our approach only uses the users’ private information locally for solving the primal subproblem hence preserves the users’ privacy. Using real-world data, we validate our proposed algorithm through extensive simulations in Matlab. The results demonstrate that our method effectively incentivizes the energy trading among the users to reduce users’ peak load and reduce the overall energy cost of the system by 23% on average.

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