Multi-Objectives Optimization of Ventilation Controllers for Passive Cooling in Residential Buildings

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In museums, poor microclimate conditions, especially large changes in relative humidity and temperature, can lead to serious deterioration of the exhibits. Properly designed heating, ventilation, and air conditioning (HVAC) systems for precise control of the air parameters are required. However, due to the financial restrictions of museums, complex air-conditioning systems are often not feasible. In this study, we tested and propose novel methods to reduce the short- and long-term fluctuations in the relative humidity in exhibition rooms of a Polish museum. The methods only include indoor temperature and ventilation airflow control strategies, without the use of (de)humidification equipment. The analysis is based on simulations using EnergyPlus software. A multi-zone thermal model of the museum building was validated and calibrated with measured data. A full calendar year was simulated for five control cases (including the current method used) and two internal heat gain schedules. The energy demand for heating and cooling for each case was calculated. The combination of temperature control and adequate ventilation using ambient airflow allows for dramatic improvement in the microclimate conditions. The proportion of the year when the instantaneous indoor relative humidity is ±5% from set point decreased from 85% to 20%. A significant effect was obtained over the summer months.

Section: Resultssupporting

confidence: 60%

Proposed Strategies for Improving Poor Hygrothermal Conditions in Museum Exhibition Rooms and Their Impact on Energy Demand

Ferdyn-Grygierek

Grygierek

2019

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“…To further evaluate occupants' thermal comfort in the proposed MBCRF algorithm, Predicted Mean Vote (PMV) is calculated in this case based on Fanger's model [44]. The absolute PMV value of 0.5 is assumed as the limitation [28]. The case study is considered under summer conditions.…”

Section: Casementioning

confidence: 99%

“…It is notable that thermal comfort should not be violated excepted for small fluctuations during the load reduction. The thermal comfort was also considered as an objective function in some articles [26][27][28]. Grygierek et al [26] proposed a multi-objective optimization to achieve the optimal selection of the envelope for a single-family building.…”

Section: Introductionmentioning

confidence: 99%

“…The energy consumption and thermal discomfort were minimized by multi-objective optimization and artificial neural networks. In [28], a fuzzy logic controller was proposed by Grygierek and Ferdyn-Grygierek. The paper exploited the modified Multiobjective Evolutionary Algorithm to limit a temperature from being too high in residential buildings. In addition, passive cooling was adopted by virtue of ventilation with ambient cool air.…”

Section: Introductionmentioning

confidence: 99%

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A MBCRF Algorithm Based on Ensemble Learning for Building Demand Response Considering the Thermal Comfort

Han

Wang

et al. 2018

Demand response (DR) has become an effective and critical method for obtaining better savings on energy consumption and cost. Buildings are the potential demand response resource since they contribute nearly 50% of the electricity usage. Currently, more DR applications for buildings were rule-based or utilized a simplified physical model. These methods may not fully embody the interaction among various features in the building. Based on the tree model, this paper presents a novel model based control with a random forest (MBCRF) learning algorithm for the demand response of commercial buildings. The baseline load of demand response and optimal control strategies are solved to respond to the DR request signals during peak load periods. Energy cost saving of the building is achieved and occupant’s thermal comfort is guaranteed simultaneously. A linguistic if-then rules-based optimal feature selection framework is also utilized to redefine the training and test set. Numerical testing results of the Pennsylvania-Jersey-Maryland (PJM) electricity market and Research and Support Facility (RSF) building show that the load forecasting error is as low as 1.28%. The peak load reduction is up to 40 kW, which achieves a 15% curtailment and outperforms rule-based DR by 5.6%.

“…Simulation tools can predict the effects of design variables on building energy consumption [3]. The studies have been performed for residential buildings and for one or different climates using TRNSYS (University of Wisconsin, Madison, WI, USA) [21,22], EnergyPlus (U.S. Department of Energy's, Washington, DC, USA) [2,23,24], DOE-2 (Lawrence Berkeley National Laboratory, Berkeley, CA, USA) [17,18], eQUEST (Energy-Models.com, San Francisco, CA, USA) [25] simulation programs.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of the Envelope of Building with Natural Ventilation

Grygierek

Ferdyn-Grygierek

2018

Self Cite

Abstract:A properly designed house should provide occupants with the high level of thermal comfort at low energy demand. On many occasions investors choose to add additional insulation to the buildings to reduce heat demand. This may lead to overheating of the building without a cooling system in summer periods (these prevail in Poland). Additionally, it affects the deterioration of thermal comfort, which can only be improved by increasing ventilation. The paper presents the multi-objective optimization of the selected design parameters in a single-family building in temperate climate conditions. The influence of four types of windows, their size, building orientation, insulation of external wall, roof and ground floor and infiltration on the life cycle costs and thermal comfort is analyzed for the building without cooling. Infiltration changes during the simulation and is controlled by a special controller. Its task is to imitate the behavior of occupants in changing the supply airflow. Optimal selection of the design parameters is carried out using Non-dominated Sorting Genetic Algorithm II (NSGA-II) by coupling the building performance simulation program EnergyPlus with optimization environment. For the single-family house, optimal values of design variables for three different criteria are presented.