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Cristino, T. Evolution of Knowledge within Building Energy Efficiency Field. Encyclopedia. Available online: https://encyclopedia.pub/entry/19007 (accessed on 17 November 2024).
Cristino T. Evolution of Knowledge within Building Energy Efficiency Field. Encyclopedia. Available at: https://encyclopedia.pub/entry/19007. Accessed November 17, 2024.
Cristino, Talita. "Evolution of Knowledge within Building Energy Efficiency Field" Encyclopedia, https://encyclopedia.pub/entry/19007 (accessed November 17, 2024).
Cristino, T. (2022, January 29). Evolution of Knowledge within Building Energy Efficiency Field. In Encyclopedia. https://encyclopedia.pub/entry/19007
Cristino, Talita. "Evolution of Knowledge within Building Energy Efficiency Field." Encyclopedia. Web. 29 January, 2022.
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The building sector is responsible for 50% of worldwide energy consumption and 40% of CO2 emissions. Consequently, a lot of research on Building Energy Efficiency has been carried out over recent years, covering the most varied topics. While many of these themes are no longer of interest to the scientific community, others flourish. However, there is a paucity of research on trend analysis in this field. Thus, it is important to read the trends within this field of knowledge is wise, since it allows resources to be directed towards the most promising topics.
energy efficiency
building energy efficiency
trend analysis
1. Introduction
Energy consumption has increased over the last decades [1][2]. Such an increase brings several concerns, such as the necessity to develop alternative energy sources and reduce environmental impacts due to greenhouse gas emissions [3][4]. The building sector has overtaken the industrial sector and has played an important role in such a scenario, being responsible for more than 50% of the total energy consumption and 40% of the total CO2 emissions [5]. In Europe, according to the European Commission [6], only residential buildings are responsible for approximately 40% of energy consumption and 36% of CO2 emissions. Therefore, it was necessary to find a way to decrease such energy consumption without affecting economic development, as well as the comfort of the building’s occupants [7][8][9]. The way that researchers found to achieve such a goal was to increase the energy efficiency of processes and products [10][11][12]. This put the building sector on the target of important public policies [13].
Thus, identifying the currently relevant topics in the Building Energy Efficiency field of knowledge is wise because resources are becoming increasingly scarce, and this means they can be directed towards the most promising themes. This also allows researchers to be able to optimise resources, investigate the topics with growing interest, and increase the possibility for new contributions.
2. The Evolution of the Building Energy Efficiency Field of Knowledge
According to Price’s Law [14], the scientific production concerned with a field of knowledge grows exponentially until it reaches a point of inflection and, afterwards, a threshold value around which it stabilises, meaning that this field has reached its maturity. The aspect of the curve that represents the evolution of publications goes from exponential to logistics, signalling that the scientific community’s interest in this field has cooled down.
According to Dabi et al. [15]: “The main hypothesis of Price’s law is that the development of science follows an exponential growth. The growth of a scientific domain goes through four phases”. The first phase is the precursors’ phase. According to Dabi et al. [15] “during this phase only a small number of researchers begin publishing”. The second phase is the proper exponential growth. “During this phase, the expansion of the field attracts many researchers as many aspects of the subject still have to be explored” [15]. In the third phase, the body of knowledge is consolidated and the growth of scientific production becomes linear [15]. The next phase, according to Dabi et al. [15], “corresponds to the collapse of the domain and is marked by a decrease in the number of the publications”. The aspect of the curve transforms from exponential to logistical, reaching a ceiling value after passing through an inflection point. Therefore, in order to perform the Price’s Law analysis, the frequency distribution of the publications addressing BEE is presented in Figure 1.
Figure 1. Frequency distribution of publications addressing Building Energy Efficiency. (a) Discrete, (b) cumulative (source: authors).
Figure 1a shows the number of publications on a yearly basis, whilst Figure 1b shows the cumulative version, on which compliance with Price’s Law is investigated.
3. Evolution and Trend of the Themes
Before studying the evolution and trend of the themes it is worth discussing their relevance over the period under investigation.
The relevance of a theme can be derived from the number of articles that address it over the period considered [16]. Thus, Table 1 presents the themes ranked according to their relevance.
Table 1. The total number of articles dealing with each theme.
| Themes | N | % | Interdisciplinarity | |
|---|---|---|---|---|
| BEM | Building energy modelling | 505 | 25.3 | 30 |
| DAT | Data analysis techniques | 337 | 16.9 | 27 |
| BIM | Building information modelling | 244 | 12.2 | 22 |
| HVAC | Heating-ventilation-air-conditioning | 159 | 8.0 | 30 |
| ENV | Environmental | 157 | 7.9 | 27 |
| GRB | Green building | 144 | 7.2 | 27 |
| TOB | Types of buildings | 130 | 6.5 | 27 |
| ZEB | Zero energy building | 128 | 6.4 | 28 |
| SMB | Smart buildings | 121 | 6.1 | 26 |
| THC | Thermal comfort | 121 | 6.1 | 28 |
| REG | Regulations | 117 | 5.9 | 26 |
| SUS | Sustainability | 114 | 5.7 | 23 |
| BRF | Building retrofitting | 105 | 5.3 | 26 |
| OCB | Occupant behaviour | 103 | 5.2 | 26 |
| BAC | Building automation and control | 99 | 5.0 | 26 |
| LCA | Life cycle assessment | 89 | 4.5 | 24 |
| SEM | Energy management systems | 87 | 4.4 | 25 |
| BIP | Building integrated photovoltaics | 83 | 4.2 | 23 |
| SMG | Smart grids | 81 | 4.1 | 23 |
| EPS | Energy performance software | 75 | 3.8 | 23 |
| BEV | Building envelope | 52 | 2.6 | 25 |
| WIN | Windows | 52 | 2.6 | 17 |
| LIG | Lighting | 52 | 2.6 | 24 |
| DMK | Decision making | 49 | 2.5 | 27 |
| RNE | Renewable energy sources | 48 | 2.4 | 26 |
| BMS | Building management systems | 36 | 1.8 | 22 |
| THS | Thermal storage | 25 | 1.3 | 14 |
| EST | Energy storage | 24 | 1.2 | 17 |
| HPS | Heat pumping systems | 17 | 0.9 | 12 |
| WTH | Water heating | 12 | 0.6 | 10 |
Table 1 provides a static view of the BEE field of knowledge. It shows the most relevant themes within the field but it does not show the evolution and trend of each theme. Thus, Table 2 presents the trend of each theme, allowing investigation as to whether a given theme has a perennial presence or is just incidental in the literature. A theme can be analysed as to when it emerged, if it is still active or vanished, and when its apogee was.
Table 2. The annual relative frequency of articles that address each of the thirty themes.
| Theme | 2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | Annual Participation | Trend (α = 5%) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| BEV | 0% | 9% | 0% | 0% | 6% | 0% | 2% | 0% | 6% | 2% | 4% | 2% | 4% | 2% | 2% | 3% | 2% | 2% | 3% |  |
↔ |
| SMB | 8% | 0% | 14% | 0% | 6% | 5% | 8% | 7% | 8% | 7% | 9% | 4% | 6% | 6% | 6% | 6% | 6% | 4% | 8% |  |
↔ |
| TOB | 8% | 0% | 14% | 6% | 6% | 5% | 0% | 7% | 10% | 4% | 9% | 7% | 6% | 6% | 7% | 5% | 9% | 7% | 4% |  |
↔ |
| BMS | 8% | 0% | 10% | 0% | 0% | 0% | 2% | 2% | 2% | 2% | 0% | 1% | 3% | 2% | 3% | 0% | 1% | 2% | 2% |  |
↔ |
| BAC | 0% | 0% | 10% | 0% | 6% | 0% | 0% | 5% | 0% | 1% | 6% | 4% | 4% | 6% | 6% | 7% | 6% | 4% | 7% |  |
↑ |
| EMS | 0% | 0% | 10% | 0% | 0% | 0% | 0% | 2% | 0% | 1% | 1% | 3% | 3% | 5% | 4% | 5% | 7% | 6% | 5% |  |
↑ |
| GRB | 8% | 0% | 0% | 19% | 0% | 15% | 2% | 15% | 14% | 6% | 7% | 10% | 6% | 7% | 7% | 10% | 6% | 5% | 5% |  |
↔ |
| HVAC | 0% | 0% | 14% | 19% | 6% | 5% | 12% | 15% | 18% | 12% | 4% | 11% | 9% | 5% | 6% | 7% | 8% | 7% | 7% |  |
↔ |
| WIN | 0% | 0% | 5% | 6% | 0% | 0% | 4% | 2% | 0% | 2% | 1% | 4% | 1% | 3% | 4% | 4% | 2% | 2% | 2% |  |
↔ |
| RNE | 0% | 0% | 0% | 6% | 0% | 0% | 0% | 0% | 2% | 0% | 0% | 2% | 3% | 4% | 2% | 3% | 4% | 1% | 5% |  |
↔ |
| DMK | 0% | 0% | 0% | 0% | 6% | 0% | 4% | 0% | 5% | 1% | 4% | 1% | 3% | 2% | 4% | 2% | 3% | 1% | 3% |  |
↔ |
| DAT | 0% | 0% | 0% | 6% | 12% | 25% | 6% | 10% | 8% | 11% | 19% | 14% | 19% | 17% | 19% | 19% | 20% | 18% | 20% |  |
↑ |
| SUS | 0% | 0% | 0% | 0% | 0% | 15% | 10% | 12% | 4% | 6% | 13% | 5% | 8% | 9% | 2% | 4% | 6% | 6% | 2% |  |
↓ |
| LCA | 0% | 0% | 5% | 6% | 0% | 10% | 8% | 7% | 4% | 1% | 4% | 7% | 3% | 5% | 3% | 3% | 5% | 7% | 5% |  |
↔ |
| EST | 0% | 0% | 0% | 0% | 0% | 5% | 2% | 2% | 0% | 0% | 0% | 2% | 1% | 3% | 0% | 1% | 2% | 1% | 2% |  |
↔ |
| LIG | 0% | 0% | 0% | 0% | 0% | 5% | 8% | 5% | 4% | 1% | 1% | 3% | 2% | 3% | 3% | 4% | 3% | 2% | 1% |  |
↓ |
| THC | 8% | 0% | 0% | 13% | 0% | 5% | 4% | 17% | 12% | 10% | 3% | 2% | 7% | 5% | 5% | 9% | 5% | 6% | 6% |  |
↔ |
| BIP | 0% | 0% | 5% | 0% | 6% | 0% | 4% | 7% | 6% | 7% | 1% | 5% | 2% | 5% | 3% | 5% | 4% | 3% | 7% |  |
↔ |
| WTH | 0% | 0% | 0% | 0% | 0% | 0% | 2% | 0% | 2% | 0% | 0% | 0% | 0% | 0% | 1% | 1% | 1% | 0% | 1% |  |
↔ |
| REG | 8% | 0% | 0% | 0% | 12% | 10% | 2% | 10% | 6% | 13% | 9% | 8% | 9% | 7% | 5% | 4% | 5% | 3% | 4% |  |
↔ |
| ENV | 8% | 0% | 5% | 0% | 6% | 10% | 8% | 5% | 12% | 12% | 10% | 8% | 5% | 9% | 7% | 9% | 8% | 8% | 5% |  |
↔ |
| EPS | 0% | 0% | 0% | 0% | 0% | 0% | 0% | 0% | 2% | 2% | 3% | 8% | 3% | 3% | 4% | 5% | 4% | 4% | 8% |  |
↔ |
| HPS | 0% | 0% | 0% | 0% | 0% | 0% | 0% | 0% | 0% | 1% | 1% | 0% | 1% | 2% | 0% | 1% | 1% | 1% | 2% |  |
↔ |
| THS | 0% | 0% | 0% | 0% | 0% | 0% | 0% | 0% | 0% | 0% | 0% | 0% | 1% | 3% | 1% | 2% | 2% | 2% | 2% |  |
↔ |
| BEM | 8% | 0% | 24% | 6% | 0% | 20% | 17% | 22% | 25% | 23% | 14% | 27% | 28% | 22% | 28% | 25% | 30% | 27% | 29% |  |
↑ |
| SMG | 0% | 0% | 0% | 0% | 0% | 0% | 0% | 0% | 2% | 5% | 0% | 2% | 5% | 3% | 6% | 3% | 7% | 3% | 6% |  |
↔ |
| BIM | 8% | 0% | 10% | 6% | 0% | 0% | 2% | 5% | 8% | 8% | 13% | 8% | 10% | 13% | 15% | 15% | 12% | 18% | 12% |  |
↑ |
| OCB | 8% | 0% | 0% | 0% | 0% | 0% | 6% | 5% | 0% | 2% | 1% | 7% | 5% | 4% | 6% | 5% | 4% | 11% | 5% |  |
↑ |
| BRF | 0% | 0% | 5% | 0% | 0% | 5% | 0% | 0% | 2% | 1% | 1% | 2% | 6% | 5% | 6% | 7% | 6% | 9% | 7% |  |
↑ |
| ZEB | 0% | 0% | 5% | 0% | 0% | 5% | 0% | 0% | 2% | 5% | 3% | 3% | 7% | 6% | 6% | 9% | 6% | 9% | 13% |  |
↑ |
Table 2 presents the annual participation of each theme in the literature, summarising their trend in the last column.
4. Stages of the Evolution of This Field of Knowledge
The evolution of a field of knowledge is marked by a sequence of periods with a similar profile of publications. Thus, reading Table 2 from the columns’ point of view, it is possible to see the profile of the years according to the themes published and look for a pattern.
One of the ways to identify similarities between multivariate observations is to apply clustering techniques [17][18]. Thus, the space of the columns in Table 2 was submitted to a hierarchical clustering algorithm, leading to the dendrogram presented in Figure 2.
Figure 2. Years grouped according to the profile of themes (source: authors).
5. Conclusions
The research identified thirty recurrent themes within this field of knowledge. However, only nineteen of these themes were considered statistically significant. According to the Mann-Kendall trend test, eight out of these themes showed a clear upward trend, one a downward trend, and ten did not show any clear evidence for a particular trend. Furthermore, the study showed that the evolution of this field of knowledge passed through three stages, whose dynamics were clearly explained, as well as the changes in the patterns of cross-fertilisation.
Energy modelling, along with data analysis techniques, has been influencing this field of knowledge since its beginning and they have been instigating production in other areas within this field. Therefore, themes like Building Energy Modelling and Data Analysis Techniques are in an upward trend and still very far from maturity, constituting good research opportunities.
Furthermore, the scientific community’s gaze is on other themes with low connections, like Occupancy Behaviour, Building Information Modelling, Zero Energy Buildings, and Building Retrofitting. All of these themes have increased in importance and seem to be new frontiers of this field of knowledge.
Considering the Occupancy Behaviour, topics like eco-feedback, gamification, behaviour, and advanced building automation systems have not been adequately addressed.
Building Information Modelling is a very recent research front, therefore there is a great interest among the scientific community in this field, which signalise that there is a great potential for research on integrating BIM with technologies like monitoring systems, thermography, geographic information systems.
The concept of Zero Energy Building has drawn the attention of the scientific community. However, there are few studies focusing on the feasibility of Zero Energy Building in a diversity of climates and the integration with information technology.
Building Energy Retrofitting provides substantial opportunities to reduce the energy consumption of the building sector. There are a lot of research opportunities such as identifying and designing optimal cost-effective energy retrofitting strategies, combining retrofitting with Building Energy Modelling, Retrofitting, and Occupancy Behaviour.
Furthermore, it is worth mentioning that some of the themes are indirectly of interest for the Zero Energy Building and Building Energy Retrofitting themes. The themes Building Management Systems, Building Envelope, Energy Performance Software, Energy Storage, Lighting, Renewable Energy Sources, Thermal Storage, and Windows could be still focused on the recent research under the umbrella of themes with increasing trends.
These findings allow the researchers to optimise time and resources by investigating the themes with growing interest and possibilities for new contributions, as the scientific community directs its efforts towards cutting edge themes and topics. Furthermore, they improve the understanding of the laws governing the development of a field of knowledge, impacting the formulation of research strategies.
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