학술논문

An IoT-Based Hedge System for Solar Power Generation
Document Type
Periodical
Source
IEEE Internet of Things Journal IEEE Internet Things J. Internet of Things Journal, IEEE. 8(13):10347-10355 Jul, 2021
Subject
Computing and Processing
Communication, Networking and Broadcast Technologies
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
Language
ISSN
2327-4662
2372-2541
Abstract
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.