Informing building integrated photovoltaics (BIPV) deployment in Australia: a sociotechnical perspective and data mining methods
Creators: name: WEERASINGHE, R.P. Nilmini
Publisher: name: RMIT University
Year: 2024
Subjects: classificationCode: 330204
subject: Building information modelling and management

description: With massive consumption of fossil energy and increasing concern about global climate change, building owners feel compelled to implement low carbon technologies. Of the energy efficiency practices and technologies available, building integrated photovoltaic (BIPV) application promises the sustainable achievement. BIPV is a renewable energy technology with a building material function, which enables the transformation of buildings into miniature power plants. However, the growth of the technology is limited compared to other photovoltaic applications. The BIPV sector is currently experiencing policy transition, high capital costs and technical and social complexities. Successful BIPV systems have been noticed particularly in Europe but a few projects in Australia. With its geographical and climatic conditions, Australia has enormous potential to harness solar energy. Learning from recent experiences may improve confidence of stakeholders on system adoption. 

Therefore, this research aim is to understand the BIPV deployment opportunities in Australia by demystifying the industry's complexity and learning from successful application. The four objectives are:1) to investigate the complex sociotechnical system of BIPV deployment 2) to evaluate decision-making performance for existing BIPV applications 3) to simulate the impact of critical sociotechnical elements on BIPV deployment and 4) to predict BIPV adoption in Australia. The scope of the research is limited to non-domestic buildings. The research adopts a mixed-method approach and both interviews and document analysis were conducted in gathering qualitative and quantitative data.

Technology adoption is not merely a technical complexity; it is linked with social concerns. Hence, the deployment is considered from a sociotechnical perspective. The sociotechnical system of BIPV adoption is transformed into a three-mode network employing metanetwork analysis by unravelling its complexity. Building owner, policymaker, building, BIPV panel and their attributes are revealed as critical sociotechnical elements in BIPV adoption, together with many drivers related to policy, economic, technical, and social aspects. 

The decisions to adopt BIPV are rolled on economic feasibility, substitutional suitability, and sustainability preferences. Building owners with heterogeneous interests place multiple weights on three decision criteria. A mathematical model is therefore built in MATLAB applications to examine system adoption decisions and impact of sociotechnical elements on deployment with the assistance of real BIPV applications. To facilitate functioning of the model, the decision criteria are transformed into quantitate decision-making performances of BIPV. Levelised cost of energy, net present value and discounted payback period is used to represent economic feasibility and material offset, and greenhouse gas emission savings represents substitutional suitability and sustainability preference, respectively. The model is executed by forty-six existing BIPV applications in western countries enabling evaluation of various performances of BIPV that drive decision scenarios. According to the findings, BIPV is unique design and favourable performance is achieved when both direct and indirect benefits are counted. Moreover, the results revealed that decisions to adopt technology are complex, and attributes of the sociotechnical elements in a highly volatile market govern multiple decisions on system adoption. It is vital to understand and foresee the adoption decisions for different conditions. A decision support model facilitating the prediction of the decision of system adoption is built, which employs support vector machine, a machine learning technique. The model is developed by MATLAB using 122 real BIPV and BAPV applications. The model is then applied to five major cities in Australia and predict possible combinations of decision determinants of BIPV acceptance.

The proposed solutions are noteworthy for the implementation of BIPV. From a theoretical perspective, this research expands the current body of knowledge of BIPV and provides an approach to the consideration of technology deployment from a sociotechnical perspective and using data-mining methods. The outcomes of the research will guide policymakers, building industry and PV industry practitioners on future directions for research, investment decisions and policymaking in relation to the adoption of BIPV.