Predictive Modelling of Building Energy System

Kangji Li

ISBN 10: 6203855413 / ISBN 13: 9786203855418

Published by LAP LAMBERT Academic Publishing, 2021

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - Building energy system modeling and energy usage prediction play an important role in fields of building energy management, power plants dispatch, and peak demand conflicting with grid security. Owing to the ease of use and adaptability of optimal solution seeking, data driven techniques have proved to be accurate and efficient tools for building and larger scale energy consumption prediction, and a large number of data-driven models were applied in the past two decades. In this book, the current research trends of energy use prediction are investigated and classified. Systematic introduction to the theoretical methods, basic steps and various application cases for building energy prediction is provided. Essential feature selection, over-fitting problem, and performance comparison is addressed. Several sets of real buildings' electricity usage data are collected for case studies including cases from energy prediction shooting organized by ASHRAE and cases from campus buildings in China and USA respectively.

Seller Inventory # 9786203855418

Bibliographic Details

• Title: Predictive Modelling of Building Energy System
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2021
• Language: English
• ISBN 10: 6203855413
• ISBN 13: 9786203855418
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 12D millimeters 220 millimeters width by 150 millimeters height by 12 millimeters depth
• Weight: 316 grams

About this title

Synopsis

Building energy system modeling and energy usage prediction play an important role in fields of building energy management, power plants dispatch, and peak demand conflicting with grid security. Owing to the ease of use and adaptability of optimal solution seeking, data driven techniques have proved to be accurate and efficient tools for building and larger scale energy consumption prediction, and a large number of data-driven models were applied in the past two decades. In this book, the current research trends of energy use prediction are investigated and classified. Systematic introduction to the theoretical methods, basic steps and various application cases for building energy prediction is provided. Essential feature selection, over-fitting problem, and performance comparison is addressed. Several sets of real buildings' electricity usage data are collected for case studies including cases from energy prediction shooting organized by ASHRAE and cases from campus buildings in China and USA respectively.