Improving Energy Performance Contracting (EPC) adoption by using smart contracts and Blockchain technologies
EPC is one of the most relevant tool for implementing energy efficiency measures in buildings. The research activity will be focused on the development of novel EPC model, considering smart contracts based on blockchain technology. The EPC definition will also consider associated business models and related side disciplines (i.e. social science and humanities).
The emergence of blockchain as secure decentralized ledgers has opened possibilities for the decentralization of the distribution of energy. The Energy Web Foundation (EWF) and a number of startups had started the process of defining the use cases, and fitting them in the current infrastructure and regulatory frameworks. However, there is still a lack of understanding of which of the emerging blockchains-related technologies supporting smart contracts (including Ethereum, R-Chain, EOS, Hyperledger and Cardano) fit the requirements of the sector. This research line aims at (1) contrasting current platforms with the requirements and use cases of the EWF for a scientifically sound understanding of the best options, and (2) design and evaluate (probably using simulation) a set of incentive alternatives using mechanism design theory that may inform industry and the scientific community.
Energy efficiency in transportation systems is a widely studied field due to its big economic and environmental impact. One of the most important problems that the research community has focused on during the last decades is the reduction of the consumed energy in all aspects of our daily life. One of the most important factors of energy consumption is transportation. To this end, a great amount of work in the field of intelligent transportation systems focuses on improving energy efficiency. Our main line of research is the study of energy efficiency in road transportation, taking into account both driver’s behavior and truck’s characteristics by using big data analysis techniques. To achieve this, models are inferred from machine learning algorithms, which are designed and implemented inside a research scope. Afterwards these models are validated with real data provided by a logistics operator specialized in dangerous goods, mainly hydrocarbon materials (in liquid state), which has a high incidence in the Spanish economy, and at the same time, has high levels of security and regulation.