| | MAY 20226E ERGYTech ReviewCopyright © 2022 ValleyMedia, Inc. All rights reserved. Reproduction in whole or part of any text, photography or illustrations without written permission from the publisher is prohibited. The publisher assumes no responsibility for unsolicited manuscripts, photographs or illustrations. Views and opinions expressed in this publication are not necessarily those of the magazine and accordingly, no liability is assumed by the publisher thereof. E ERGYTech Review MAY - 12 - 2022, Vol 05 - Issue 03 Published by ValleyMedia, Inc. To subscribe to Energy Tech ReviewVisit www.energytechreview.com SalesRich Gonsalvesrich@energytechreview.comVisualizersAsher BlakeManaging EditorCharlotte SmithE ERGYTech ReviewEDITOR'S DESKAs solar energy technology advances, its use in a variety of products is becoming more widespread and affordable. Solar-powered LED streetlights, for example, reduce energy bills and carbon emissions while safeguarding public safety and encouraging energy efficiency in many urban locations. While generating renewable energy, PV noise barriers around highways make adjacent neighborhoods more habitable. In addition, solar panels are increasingly being used to power EV chargers.With new technologies emerging to suit the expanding needs in the solar energy business, Machine learning, such as through microgrid controllers and artificial intelligence, is one of the trends that would dominate. The new software is also influencing how firms may use AI and machine learning in solar energy technology. AI is increasingly used by companies in this industry to identify and track patterns in energy generation and consumption. It will also aid in a better understanding of customer behavior. The exchange of real-time data and real-time monitoring of electricity consumption will provide providers with more information on consumer behavior towards electricity costs as well as their precise electricity needs. Furthermore, with increased access to power system operating data, AI implementation has increased significantly, with enhanced accuracy. The collected data is then used to enable AI-assisted learning methodologies for detecting various difficulties and irregularities in the system and taking suitable action within the timeframe.This edition brings you some of the prominent enterprises that have been instrumental in transforming the landscape of energy technologies and have excelled with their service and will be significant drivers of the trends mentioned above to the mainstream.Let us know your thoughts!Charlotte SmithManaging Editoreditor@energytechreview.comSpearheading Innovation in the Solar Industry*Some of the Insights are based on our interviews with CIOs and CXOsEditorial StaffAaron Pierce Ava GarciaVian IsaacJoshua Parker Kenny PeruzziEmailsales@energytechreview.comeditor@energytechreview.commarketing@energytechreview.com
< Page 5 | Page 7 >