BigData techniques for Solar Power Forecasting | A SMART Grid approach to Solar Power Forecasting

Rui Nunes

ISBN 10: 6206780155 / ISBN 13: 9786206780151

Published by LAP LAMBERT Academic Publishing, 2023

About this Item

BigData techniques for Solar Power Forecasting | A SMART Grid approach to Solar Power Forecasting | Rui Nunes | Taschenbuch | Englisch | 2023 | LAP LAMBERT Academic Publishing | EAN 9786206780151 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Seller Inventory # 127714931

Bibliographic Details

• Title: BigData techniques for Solar Power Forecasting | A SMART Grid approach to Solar Power Forecasting
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2023
• Language: English
• ISBN 10: 6206780155
• ISBN 13: 9786206780151
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 7D millimeters 220 millimeters width by 150 millimeters height by 7 millimeters depth
• Weight: 173 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

With the growth of social awareness for environmental issues, came a new and important problem to the discussion, related to renewable energies. Although this kind of energy production is already in an advanced state of development there are some issues that still need to be studied and perfected on the real production environment. The Photovoltaic production is gain some importance but the prediction of this kind of energy still need a boost in their performance. The SMART Grid projects brought this issues to another level where the importance of these predictions needs to be as accurate as possible, butthey brought another problem, how to accurate this prediction with all data that this kindof problems is associated with. There are some accurate models for a single location prediction but we involving multiple locations in one problem we need to have some computational power. Big Data techniques such the Convex optimization are a field that is important to explore since these techniques applied with the parallel computer programming paradigm can bring that computational power, and more accurate models can be trained faster.