Statistical Understanding and Optimization of Building Energy Consumption and Climate Change Consequences

Statistical Understanding and Optimization of Building Energy Consumption and Climate Change Consequences

José Antonio Orosa, Diego Vergara, Feliciano Fraguela, Antonio Masdías-Bonome
Copyright: © 2021 |Pages: 26
DOI: 10.4018/978-1-7998-7023-4.ch009
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Abstract

In the present chapter, a new tool was designed to find a better alternative for improving building energy consumption in the next years. In this sense, in the first stage of this calculation procedure, ISO Standard 13790 calculation procedure was developed in accordance with Monte Carlo method and results showed the probability of energy consumption as a Weibull model. Furthermore, a map of different Weibull models in accordance with different input parameters and future climate change effect was developed as a future building design guide. This tool defines the probability of energy consumption of an existing building, or a building that is being designed today and in the near future, preventing the climate change effect. More applications at the time of building retrofitting and healthy indoor ambiences are proposed.
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Introduction

In accordance with the International Energy Agency (IEA), one-third of the energy consumption is in indoor ambiences. In this sense, as a consequence of the energy consumption, different humanity problems like climate change, greenhouse gas effect and some types of contaminations are related to the burn of fossil fuels. The problem is related to the fact that these combustibles have higher calorific power respect its weight and, in consequence, it is usually employed in transportation. These same engines employed in maritime transport use to be employed in Power plants due to the higher the thermal focus, the higher the efficiency of the process. In consequence, the electricity generated by these power plants and cogeneration plants is distributed to nearby cities emitting carbon dioxide emissions. As a consequence of burning this fossil fuels, carbon dioxide emissions increase and the previous commented problems will remain.

Despite the new renewable resources, it is possible to complement the energy consumption in buildings with solar energy, wind energy or by improving thermal inertia and constructive characteristics in accordance with the international Energy Agency objectives called “Nearly Zero Energy Buildings” (NZEB).

Energy consumption in indoor ambiences is related with different parameters like, for instance, building characteristics (orientation, thermal inertial, …), air conditioning systems (HVAC) and habits of occupants, between others. All these concepts are grouped into standards that try to define a mathematical model of heat and mass transfer in buildings with the aim to be an optimization tool in the future. The interest of the scientific community is reflected in the growing trend of the number of articles published in recent years which are related to energy consumption in buildings. As an example, in Figure 1 is represented the number of articles indexed in SCOPUS in the last decade through the following searching: (TITLE-ABS-KEY (“energy consumption”)) AND (building). Thus, a growing trend is exposed in Figure 1, which suggests the number of articles related to this topic is increasing about 450 per year (indexed in Scopus database).

Figure 1.

Growing trend in the number of articles indexed in Scopus related to the energy consumption in buildings

978-1-7998-7023-4.ch009.f01

Climate Change is a subsequent problem related with previous commented. Despite the fact it was neglected in the last decades, nowadays there are few doubts about its origin and its consequences now and in a near future. Once again, the combustion of fossil fuels and deforestation are related with greenhouse gases and these are related with a change in natural processes like the Gulf Stream which, as a consequence of the heating, is weaker. In consequence, a lower amount of warm water is sent to the Gulf of Mexico, U.S. and Western Europe. The main translation of this climate change is a change of weathers seasons from four to two extreme; just one hot and one cold weather seasons (Orosa, Costa, Rodríguez-Fernández and Roshan, 2014). This effect was observed in different regions and its initial consequences are an increment of average outdoor temperature. This will imply a modification of rainy seasons and extreme temperatures making wrong most of building designs with an error margin that increases with years. Despite this, few research works investigate this effect due to, in an initial period, an increase of no more than one degree in ten or more years was observed. The problem arrives when this average outdoor temperature increases grows exponentially and non in a linear way. In consequence, few calculation procedures and improvement proposals to prevent or correct this variation in existing buildings are defined.

Recent research works have shown that, in the last years and in the near future, there was and will be an increment in the mean outdoor temperature, and also how to minimize the energy consumption in the next 30 years. Some of the consequences of this increment are related to an increase in the cooling energy consumption and increase of general discomfort in the indoor environments (Orosa and Oliveira, 2010; Roshan, Ghanghermeh and Orosa, 2014; Ye et al., 2021).

Key Terms in this Chapter

Indoor Ambience: It is the state of the atmosphere in closed places.

Energy Rating Software: Computer application employed to define the level of efficiency of a building energy consumption process.

Climate Change: A modification in climate patterns that may affect a region or the whole Earth planet.

Ventilation: Process to introduce outdoor air into an indoor ambience.

Building Energy Consumption: The amount of energy consumed in Heating Ventilation and air conditioning of indoor ambiences.

Optimization: Process to define the optimal state of a system in accordance with previously fixed objectives.

Weibull Model: Continuous statistical distribution usually employed to define real life data.

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