Building energy savings: Analysis of research trends based on text mining

Building energy saving has become the top concern in achieving global sustainability. In the past decades, massive amounts of academic articles and engineering reports have been published, focusing on the energy conservation throughout the whole building life-cycle. From a macroscopic perspective, these articles provide a comprehensive description on the development of building energy saving measures and technologies. The knowledge discovered from such text data can be used to facilitate the decision-making for researchers and policymakers. Conventional approaches are neither effective nor efficient in analyzing massive text data. As a solution, this study proposes a text mining-based methodology to gain insights from relevant literature on building energy saving. In total, 1600 articles were collected and analyzed at different stages according to important timestamps identified. Various text mining techniques were adopted to identify and describe the research trends. The results present clear differences in research focuses at different stages. An emerging research trend has been identified in the building field, which is related to green buildings, intelligent buildings and low-carbon buildings. The methodology developed in this study can be used as a prototype to enable semi-automated knowledge discovery from massive text data in the building field.