Blockchain + IoT sensor network to measure, evaluate and incentivize personal environmental accounting and efficient energy use in indoor spaces

计算机科学 智能电网 块链 能源消耗 环境经济学 智能合约 灵活性(工程) 计算机安全 工程类 数学 统计 电气工程 经济
作者
Nan Ma,Alex Waegel,Max Hakkarainen,William W. Braham,Lior Glass,Dorit Aviv
出处
期刊:Applied Energy [Elsevier BV]
卷期号:332: 120443-120443 被引量:31
标识
DOI:10.1016/j.apenergy.2022.120443
摘要

Electric demand flexibility in buildings is highly dependent on occupant behavior. Evaluating and incentivizing these behaviors can provide grid-responsive support and encourage demand response (DR) participation. To achieve these goals, we developed an infrastructure for connecting Internet of Things (IoT) sensors to a distributed ledger (blockchain network) for long-term monitoring of energy and environmental performance. This study presents a novel Blockchain + IoT paradigm for the building science research community, applied in a real-world application. This Blockchain + IoT Network (BIN) uses Raspberry Pi minicomputers as platforms for connecting sensors to a blockchain network, to provide and analyze real-time indoor environmental quality (IEQ), energy, and carbon intensity data. As part of the study, we propose various metrics to evaluate the environmental footprints of building users. Novel algorithms for normalizing energy usage and carbon intensity, with consideration of a variety of related environmental factors, are executed as smart contracts on the blockchain network. All measurements and the smart contract transactions are reported and visualized on live dashboards. The use of smart contract allocates tokens based on the reward algorithms to incentivize individuals’ energy conservation, and similarly to DR pricing, can help influence occupant consumption patterns towards carbon reduction goals. We further test the smart contract’s algorithm in relation to real sensor data we have collected in two case studies: single-unit households and carbon intensity in the energy market. The combination of proposed metrics translates measured sensor data into token awards, demonstrates upper and lower limits dictated by the grid generation mix profile, and indicates that there is the potential for load shifting to minimize carbon emissions without considering the scale of consumption.
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