An energy-efficient predictive control for HVAC systems applied to tertiary buildings based on regression techniques

Manjarres, Diana; Mera, Ana; Perea, E.; Lejarazu, Adelaida; Gil-López, Sergio

doi:10.1016/j.enbuild.2017.07.056

Cited by 58 publications

(15 citation statements)

References 15 publications

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“…Different input streams such as weather forecast, energy prices, and occupancies built on D. Manjarres et al [46]' study. A serious is evaluated by A. Schieweck et al [47] of existing sensor technologies, Indoor air quality and hygiene are currently considered more seriously and considered as main aspects of smart home technologies.…”

Section: Control Systems Automationmentioning

confidence: 99%

Novel Techniques for Saving Energy Consumption in Air Conditioning Systems

Berry¹

2019

Proceedings of the 5th World Congress on Mechanical, Chemical, and Material Engineering

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Electrical energy consumption in air conditioning systems (ACS) is a sizeable portion of global electrical energy consumption. This means that it has a considerable share of global warming, which needs to be reduced. This article reviews the causes of excessive ACS energy consumption as well as modern trends in management and control of ACS energy consumption. Energy demand and consumption are high for air conditioning, especially in humid and hot weather. Opportunities for saving energy in cooling and heating systems depend mainly on reducing loads. Reducing the thermal loads can be achieved through new techniques Brief informational review of some of the most used ACS specifies cooling and heating loads and new techniques to reduce its energy consumption. Performance of ACSs for various types is mostly identified as the ratio of the active or beneficial output to the requisite total input of the equipment. The target to decrease the energy consumption of electrical systems is an essential step to define the components and equipment that are used to build the system and select the types that are considered as an energy saver. After ending this essential step we can concentrate on selecting and improving the overall system performance. Hence, this article provides review information of the most ACSs and specify its new techniques of energy consumption reduction.

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Section: Control Systems Automationmentioning

confidence: 99%

Novel Techniques for Saving Energy Consumption in Air Conditioning Systems

Berry¹

2019

Proceedings of the 5th World Congress on Mechanical, Chemical, and Material Engineering

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“…Therefore, the reduction of building energy consumption is crucial in order to achieve the goals in terms of decarbonization recently established in Paris Agreement. To this scope, several actions are required, such as: building envelope refurbishments [140][141][142], optimization of the HVAC systems [143][144][145][146][147][148][149], utilization of renewable energy sources [150][151][152][153][154][155][156]. This topic was initially marginally investigated during the first SDEWES conferences.…”

Section: Building Efficiencymentioning

confidence: 99%

Recent Advances in the Analysis of Sustainable Energy Systems

et al. 2018

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EU energy policy is more and more promoting a resilient, efficient and sustainable energy system. Several agreements have been signed in the last few months that set ambitious goals in terms of energy efficiency and emission reductions and to reduce the energy consumption in buildings. These actions are expected to fulfill the goals negotiated at the Paris Agreement in 2015. The successful development of this ambitious energy policy needs to be supported by scientific knowledge: a huge effort must be made in order to develop more efficient energy conversion technologies based both on renewables and fossil fuels. Similarly, researchers are also expected to work on the integration of conventional and novel systems, also taking into account the needs for the management of the novel energy systems in terms of energy storage and devices management. Therefore, a multi-disciplinary approach is required in order to achieve these goals. To ensure that the scientists belonging to the different disciplines are aware of the scientific progress in the other research areas, specific Conferences are periodically organized. One of the most popular conferences in this area is the Sustainable Development of Energy, Water and Environment Systems (SDEWES) Series Conference. The 12th Sustainable Development of Energy, Water and Environment Systems Conference was recently held in Dubrovnik, Croatia. The present Special Issue of Energies, specifically dedicated to the 12th SDEWES Conference, is focused on five main fields: energy policy and energy efficiency in smart energy systems, polygeneration and district heating, advanced combustion techniques and fuels, biomass and building efficiency.

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“…From the results of the study, it was found that the monthly average outdoor dry-bulb temperature was the most important variable that affected model accuracy, and a simple linear regression model was sufficient for the simulation of energy use. Manjarres et al (2017) proposed an optimal energy-efficient predictive control framework to achieve the minimization of HVAC power consumption, and compared energy savings through the use of an energy baseline. Kissock and Kelly (1993) used four weather parameters to construct energy consumption models for the prediction of energy use in commercial buildings.…”

Section: Introductionmentioning

confidence: 99%

Applying the naïve Bayes classifier to HVAC energy prediction using hourly data

Lin

Liu

et al. 2019

Microsyst Technol

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Heating ventilation and air conditioning (HVAC) accounts for approximately 50% of the total energy consumption of buildings. Therefore, many studies have been focused on the simulation and optimal control of HVAC power consumption or the prediction of energy consumption through the construction of energy consumption models and the improvement of HVAC power consumption through energy management methods. The prediction of energy consumption by optimal energy-saving control or energy baseline is dependent on an accurate energy consumption model, however, the accuracy of the energy consumption model is influenced by the model variables. In addition, different operating periods and load conditions also lead to different changes in energy consumption, which will affect the accuracy of optimal energy consumption control or prediction of energy consumption. The present study proposes a method to enhance the accuracy and sensitivity of HVAC power consumption prediction, which involves the use of a clustering technique to locate clusters with similar information within hourly data, the construction of energy consumption models by converting the clustered hourly data into monthly data, and the application of the proposed Naïve Bayes classifier to classify hourly data under different operating conditions into the energy consumption model with the smallest prediction error. A multiple variable regression model and an artificial neural network (ANN) model were compared with the models developed in the present study, and the normalized mean bias error (NMBE) and the coefficient of variation of the root mean squared error (Cv-RMSE) were used as criteria for the predicted energy consumption values.

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An energy-efficient predictive control for HVAC systems applied to tertiary buildings based on regression techniques

Cited by 58 publications

References 15 publications

Novel Techniques for Saving Energy Consumption in Air Conditioning Systems

Novel Techniques for Saving Energy Consumption in Air Conditioning Systems

Recent Advances in the Analysis of Sustainable Energy Systems

Applying the naïve Bayes classifier to HVAC energy prediction using hourly data

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