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Novel IoT Techniques in Renewable Energy/Power Systems
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Novel IoT Techniques in Renewable Energy/Power Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (20 December 2023) | Viewed by 1007

Special Issue Editor

Prof. Dr. Subramaniyaswamy Vairavasundaram

E-Mail Website
Guest Editor
School of Computing, SASTRA Deemed University, Thanjavur 613401, Tamilnadu, India
Interests: artificial intelligence; machine learning; deep learning; recommender system; security; IoT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the era of Industry 4.0, the world is increasingly becoming smarter as everything from mobile phones to autonomous vehicles to smart cities connects with unique addresses and communication mechanisms. The Internet of Things (IoT) paradigm is increasingly integrated with real-world applications. Many critical IoT applications exist in the renewable energy/power systems sector. Worldwide renewable energy/power systems and infrastructure are experiencing tremendous transformation. There has been a drastic surge in global energy consumption, which has doubled in the past 20 years. As a result, new measures have been introduced to improve the responsiveness and robustness of renewable energy/power systems, along with the global trends of deregulation and decarbonization. Renewable energy/power systems, covering energy generation, transmission, distribution, and consumption processes in various energy media sectors such as power grids, smart buildings, smart homes, transportation systems, and smart cities, are among the most important applications of IoT-based solutions. The integration of renewable energies (photovoltaic solar, wind energy, biomass energy, hydroelectric energy, and other sources) in smart grids implies the monitoring of households, cities, industries, and electric vehicles at all times. There is a heightened need for renewable energy/power systems for real-time data analytics, dynamic control, and disruption mitigation, which can be empowered by various novel IoT technologies.

This SI aims to create a forum for researchers, developers, and practitioners from academia and industry to disseminate state-of-the-art results and advance IoT applications for renewable energy/power systems.

Prof. Dr. Subramaniyaswamy Vairavasundaram
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Internet of Things in the Energy Sector
  • Renewable Energy Integration 
  • Smart Grids and Smart Cities
  • Electric Vehicles and Battery Management System
  • Power System Digitalization
  • IoT data analytics for Renewable Energy/Power Systems
  • Machine Learning
  • IoT-based optimization and control for renewable energy/power systems
  • IoT-based solutions for energy storage and electric/alternative energy vehicle management
  • IoT-based energy management for data centers, smart homes, smart buildings, and smart cities

Published Papers (1 paper)

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Research

15 pages, 1801 KiB  
Article
Two-Stage Model-Based Predicting PV Generation with the Conjugation of IoT Sensor Data
by Youngju Heo, Jangkyum Kim and Seong Gon Choi
Sensors 2023, 23(22), 9178; https://doi.org/10.3390/s23229178 - 14 Nov 2023
Viewed by 695
Abstract
This paper proposes a novel short-term photovoltaic voltage (PV) prediction scheme using IoT sensor data with the two-stage neural network model. It is efficient to use environmental data provided by the meteorological agency to predict future PV generation. However, such environmental data represent [...] Read more.
This paper proposes a novel short-term photovoltaic voltage (PV) prediction scheme using IoT sensor data with the two-stage neural network model. It is efficient to use environmental data provided by the meteorological agency to predict future PV generation. However, such environmental data represent the average value of the wide area, and there is a limitation in detecting environmental changes in the specific area where the solar panel is installed. In order to solve such issues, it is essential to establish IoT sensor data to detect environmental changes in the specific area. However, most conventional research focuses only on the efficiency of IoT sensor data without taking into account the timing of data acquisition from the sensors. In real-world scenarios, IoT sensor data is not available precisely when needed for predictions. Therefore, it is necessary to predict the IoT data first and then use it to forecast PV generation. In this paper, we propose a two-stage model to achieve high-accuracy prediction results. In the first stage, we use predicted environmental data to access IoT sensor data in the desired future time point. In the second stage, the predicted IoT sensors and environmental data are used to predict PV generation. Here, we determine the appropriate prediction scheme at each stage by analyzing the model characteristics to increase prediction accuracy. In addition, we show that the proposed prediction scheme could increase prediction accuracy by more than 12% compared to the baseline scheme that only uses a meteorological agency to predict PV generation. Full article
(This article belongs to the Special Issue Novel IoT Techniques in Renewable Energy/Power Systems)
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