Energy Consumption Influenced By Occupant Behavior: A Study In Residential Buildings In Panama

Building occupants are considered as a major source of uncertainty in energy modeling nowadays. Yet, industrial energy simulation tools often account for occupant behavior through some predefined scenarios and fixed consumption profiles which yield to unrealistic and inaccurate predictions. In this paper, a stochastic activity-based approach for forecasting occupant-related energy consumption in residential buildings is proposed. First, the model is exposed together with its different variables. Second, a direct application of the model on the domestic activity “washing laundry” is performed. A number of simulations are performed and their results are presented and discussed. Finally, the model is validated by confronting simulation results to real measured data.

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Impact of Window-to-Wall Ratio on Heating Demand and Thermal Comfort When Considering a Variety of Occupant Behavior Profiles

Frontiers in Sustainable Cities ◽

10.3389/frsc.2021.700794 ◽

2021 ◽

Vol 3 ◽

Author(s):

Debby Veillette ◽

Jean Rouleau ◽

Louis Gosselin

Keyword(s):

Energy Consumption ◽

Thermal Comfort ◽

Residential Buildings ◽

Probability Distributions ◽

Building Design ◽

Occupant Behavior ◽

High Level ◽

The Impact ◽

Behavior Profiles ◽

Selection Of

Energy consumption and thermal comfort in residential buildings are highly influenced by occupant behavior, which exhibits a high level of day-to-day and dwelling-to-dwelling variance. Although occupant behavior stochastic models have been developed in the past, the analysis or selection of a building design parameter is typically based on simulations that use a single “average” occupant behavior schedule which does not account for all possible profiles. The objective of this study is to enhance the understanding of how window-to-wall ratio (WWR) of a residential unit affects heating demand and thermal comfort when considering occupant behavior diversity through a parametric analysis. To do so, a stochastic occupant behavior model generates a high number of possible profiles, which are then used as input in an energy simulation of the dwelling. As a result, one obtains probability distributions of energy consumption and comfort for different WWR values. The paper shows that the shape of the probability distributions is affected by WWR and dwelling orientation, and that the influence of different occupant behavior aspects on performance also varies with WWR. This work could help designers to better assess the impact of WWR for a large spectrum of possible occupant behavior profiles.

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Daily Occupant Behavior Pattern Affecting Energy Use in Residential Buildings

Construction Research Congress 2020 ◽

10.1061/9780784482865.055 ◽

2020 ◽

Author(s):

Yunjeong Mo ◽

Dong Zhao ◽

James Sorce

Keyword(s):

Energy Use ◽

Behavior Pattern ◽

Residential Buildings ◽

Occupant Behavior

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Energy Consumption Pattern of Residential Buildings: Case Study of Residential Area in Batu Pahat, Johor

International Journal of Integrated Engineering ◽

10.30880/ijie.2020.12.09.007 ◽

2020 ◽

Vol 12 (9) ◽

Author(s):

Junaidah Jailani ◽

◽

Norsyalifa Mohamad ◽

Muhammad Amirul Omar ◽

Hauashdh Ali ◽

...

Keyword(s):

Energy Consumption ◽

Residential Buildings ◽

Residential Area ◽

Consumption Pattern ◽

Air Conditioners ◽

Residential Sector ◽

Waste Energy ◽

Main Factor ◽

Energy Consumption Pattern

According to the National Energy Balance report released by the Energy Commission of Malaysia in 2016, the residential sector uses 21.6% of the total energy in Malaysia. Residents waste energy through inefficient energy consumption and a lack of awareness. Building occupants are considered the main factor that influences energy consumption in buildings, and to change energy consumption on an overall scale, it is crucial to change individual behaviour. Therefore, this study focused on analysing the energy consumption pattern and the behaviour of consumers towards energy consumption in their homes in the residential area of Batu Pahat, Johor. A self-administrated questionnaire approach was employed in this study. The findings of this study showed that the excessive use of air conditioners was a significant factor in the increasing electricity bills of homeowners as well as the inefficient use of electrical appliances. Also, this study determined the effect of awareness on consumer behaviour. This study recommends ways to help minimise energy consumption in the residential area.

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Predicting energy consumption for residential buildings using ANN through parametric modeling

Energy Reports ◽

10.1016/j.egyr.2021.04.053 ◽

2021 ◽

Vol 7 ◽

pp. 2534-2545

Author(s):

Emad Elbeltagi ◽

Hossam Wefki

Keyword(s):

Energy Consumption ◽

Residential Buildings ◽

Parametric Modeling

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The Effect of the Thermal Mass of the Building Envelope on Summer Overheating of Dwellings in a Temperate Climate

Energies ◽

10.3390/en14144117 ◽

2021 ◽

Vol 14 (14) ◽

pp. 4117

Author(s):

Tadeusz Kuczyński ◽

Anna Staszczuk ◽

Piotr Ziembicki ◽

Anna Paluszak

Keyword(s):

Building Materials ◽

Heat Waves ◽

Residential Buildings ◽

Building Envelope ◽

Temperate Climate ◽

Maximum Temperature ◽

Thermal Mass ◽

Occupant Behavior ◽

Average Maximum ◽

Night Ventilation

The main objective of this paper is to demonstrate the effectiveness of increasing the thermal capacity of a residential building by using traditional building materials to reduce the risk of its excessive overheating during intense heat waves in a temperate climate. An additional objective is to show that the use of this single passive measure significantly reduces the risk of overheating in daytime rooms, but also, though to a much lesser extent, in bedrooms. Increasing the thermal mass of the room from light to a medium heavy reduced the average maximum daily temperature by 2.2K during the first heat wave and by 2.6K during the other two heat waves. The use of very heavy construction further reduced the average maximum temperature for the heat waves analyzed by 1.4K, 1.2K and 1.7K, respectively, giving a total possible reduction in maximum daily temperatures in the range of 3.6 °C, 3.8 °C and 4.3 °C. A discussion of the influence of occupant behavior on the use of night ventilation and external blinds was carried out, finding a significant effect on the effectiveness of the use of both methods. The results of the study suggest that in temperate European countries, preserving residential construction methods with heavy envelopes and partitions could significantly reduce the risk of overheating in residential buildings over the next few decades, without the need for night ventilation or external blinds, whose effectiveness is highly dependent on individual occupant behavior.

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Ensemble Prediction Approach Based on Learning to Statistical Model for Efficient Building Energy Consumption Management

Symmetry ◽

10.3390/sym13030405 ◽

2021 ◽

Vol 13 (3) ◽

pp. 405

Author(s):

Anam Nawaz Khan ◽

Naeem Iqbal ◽

Rashid Ahmad ◽

Do-Hyeun Kim

Keyword(s):

Energy Consumption ◽

Statistical Model ◽

Residential Buildings ◽

Residential Building ◽

Experimental Results ◽

Consumption Patterns ◽

Ensemble Prediction ◽

Short Term ◽

Term Energy ◽

Prediction Approach

With the development of modern power systems (smart grid), energy consumption prediction becomes an essential aspect of resource planning and operations. In the last few decades, industrial and commercial buildings have thoroughly been investigated for consumption patterns. However, due to the unavailability of data, the residential buildings could not get much attention. During the last few years, many solutions have been devised for predicting electric consumption; however, it remains a challenging task due to the dynamic nature of residential consumption patterns. Therefore, a more robust solution is required to improve the model performance and achieve a better prediction accuracy. This paper presents an ensemble approach based on learning to a statistical model to predict the short-term energy consumption of a multifamily residential building. Our proposed approach utilizes Long Short-Term Memory (LSTM) and Kalman Filter (KF) to build an ensemble prediction model to predict short term energy demands of multifamily residential buildings. The proposed approach uses real energy data acquired from the multifamily residential building, South Korea. Different statistical measures are used, such as mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and R2 score, to evaluate the performance of the proposed approach and compare it with existing models. The experimental results reveal that the proposed approach predicts accurately and outperforms the existing models. Furthermore, a comparative analysis is performed to evaluate and compare the proposed model with conventional machine learning models. The experimental results show the effectiveness and significance of the proposed approach compared to existing energy prediction models. The proposed approach will support energy management to effectively plan and manage the energy supply and demands of multifamily residential buildings.

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Towards Sustainable Energy Retrofitting, a Simulation for Potential Energy Use Reduction in Residential Buildings in Palestine

Energies ◽

10.3390/en14133876 ◽

2021 ◽

Vol 14 (13) ◽

pp. 3876

Author(s):

Sameh Monna ◽

Adel Juaidi ◽

Ramez Abdallah ◽

Aiman Albatayneh ◽

Patrick Dutournie ◽

...

Keyword(s):

Energy Consumption ◽

Energy Use ◽

Energy Savings ◽

Residential Buildings ◽

Residential Building ◽

Simulation Models ◽

Water Heating ◽

Heating And Cooling ◽

Space Cooling ◽

Cooling Loads

Since buildings are one of the major contributors to global warming, efforts should be intensified to make them more energy-efficient, particularly existing buildings. This research intends to analyze the energy savings from a suggested retrofitting program using energy simulation for typical existing residential buildings. For the assessment of the energy retrofitting program using computer simulation, the most commonly utilized residential building types were selected. The energy consumption of those selected residential buildings was assessed, and a baseline for evaluating energy retrofitting was established. Three levels of retrofitting programs were implemented. These levels were ordered by cost, with the first level being the least costly and the third level is the most expensive. The simulation models were created for two different types of buildings in three different climatic zones in Palestine. The findings suggest that water heating, space heating, space cooling, and electric lighting are the highest energy consumers in ordinary houses. Level one measures resulted in a 19–24 percent decrease in energy consumption due to reduced heating and cooling loads. The use of a combination of levels one and two resulted in a decrease of energy consumption for heating, cooling, and lighting by 50–57%. The use of the three levels resulted in a decrease of 71–80% in total energy usage for heating, cooling, lighting, water heating, and air conditioning.

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