Journal
IEEE INTERNET OF THINGS JOURNAL
Volume 8, Issue 13, Pages 10347-10355Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2021.3064384
Keywords
Predictive models; Support vector machines; Data models; Computational modeling; Internet of Things; Investment; Business; Edge computing; hedging; Internet of Things (IoT)-based model; machine learning; solar-power generation
Categories
Funding
- National Center for Research and Development through the Project Automated Guided Vehicles integrated with Collaborative Robots for Smart Industry Perspective [NOR/POLNOR/CoBotAGV/0027/2019-00]
- Western Norway University of Applied Sciences, Bergen, Norway
- Ministry of Science and 682 Technology (MOST), Taiwan [MOST 109-2221-E-027-106]
Ask authors/readers for more resources
The article proposes a hedging system using IoT-based data and edge predictive models to hedge low-radiation risk for solar investors, and its effectiveness is demonstrated through experiments.
Environmental protection is an important issue in recent decades, and renewable energy is an ideal solution for eco-friendly power generation. Solar-power generation is a popular renewable energy with low cost and small environmental footprint, which leads to exponential growth and high industrial investment. A mature solar business model has been established, but some uncertainties hinder the development, especially when focusing on the lack of solar-radiation. To address these issues, in this article we propose a hedging system to hedge the low-radiation risk for solar-investors through the designed IoT-based data, edge-based models for predicting solar-radiation as well as hedging options. Our experimental results show that the edge-based predictive models can obtain an R-squared value of 0.841 and a correlation coefficient of 0.917. For binary options designed in the hedging system, the broker can obtain stable payoffs with the highest Sharpe ratio of 3.354, and the investors can obtain large payoffs during low-radiation. Our simulation results show the effectiveness of the proposed hedging system for investors (buyer-side), simultaneously, present the motivation of the broker (seller-side) to join the designed hedging system utilized in solar-power generation.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available