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Author: Dirk Deschrijver
Publisher: Mdpi AG
ISBN: 9783036512075
Category :
Languages : en
Pages : 218
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Book Description
In October 2014, the EU leaders agreed upon three key targets for the year 2030: a reduction by at least 40% in greenhouse gas emissions, savings of at least 27% for renewable energy, and improvements by at least 27% in energy efficiency. The increase in computational power combined with advanced modeling and simulation tools makes it possible to derive new technological solutions that can enhance the energy efficiency of systems and that can reduce the ecological footprint. This book compiles 10 novel research works from a Special Issue that was focused on data-driven approaches, machine learning, or artificial intelligence for the modeling, simulation, and optimization of energy systems.
Author: Dirk Deschrijver
Publisher: Mdpi AG
ISBN: 9783036512075
Category :
Languages : en
Pages : 218
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Book Description
In October 2014, the EU leaders agreed upon three key targets for the year 2030: a reduction by at least 40% in greenhouse gas emissions, savings of at least 27% for renewable energy, and improvements by at least 27% in energy efficiency. The increase in computational power combined with advanced modeling and simulation tools makes it possible to derive new technological solutions that can enhance the energy efficiency of systems and that can reduce the ecological footprint. This book compiles 10 novel research works from a Special Issue that was focused on data-driven approaches, machine learning, or artificial intelligence for the modeling, simulation, and optimization of energy systems.
Author: Anna Carina Römer
Publisher: Springer Nature
ISBN: 3658329718
Category : Business & Economics
Languages : en
Pages : 221
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Book Description
The importance of the energy and commodity markets has steadily increased since the first oil crisis. The sustained use of energy and other resources has become a basic requirement for a company to competitively perform on the market. The modeling, analysis and assessment of dynamic production processes is often performed using simulation software. While existing approaches mainly focus on the consideration of resource consumption variables based on metrologically collected data on operating states, the aim of this work is to depict the energy consumption of production plants through the utilization of a continuous simulation approach in combination with a discrete approach for the modeling of material flows and supporting logistic processes. The complex interactions between the material flow and the energy usage in production can thus be simulated closer to reality, especially the depiction of energy consumption peaks becomes possible. An essential step towards reducing energy consumption in production is the optimization of the energy use of non-value-adding production phases.
Author: Saleh Seyedzadeh
Publisher: Springer Nature
ISBN: 303064751X
Category : Architecture
Languages : en
Pages : 161
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Book Description
This book outlines the data-driven modelling of building energy performance to support retrofit decision-making. It explains how to determine the appropriate machine learning (ML) model, explores the selection and expansion of a reasonable dataset and discusses the extraction of relevant features and maximisation of model accuracy. This book develops a framework for the quick selection of a ML model based on the data and application. It also proposes a method for optimising ML models for forecasting buildings energy loads by employing multi-objective optimisation with evolutionary algorithms. The book then develops an energy performance prediction model for non-domestic buildings using ML techniques, as well as utilising a case study to lay out the process of model development. Finally, the book outlines a framework to choose suitable artificial intelligence methods for modelling building energy performances. This book is of use to both academics and practising energy engineers, as it provides theoretical and practical advice relating to data-driven modelling for energy retrofitting of non-domestic buildings.
Author: Liangzhu Leon Wang
Publisher: Springer Nature
ISBN: 9811998221
Category : Technology & Engineering
Languages : en
Pages : 2933
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Book Description
This book is a compilation of selected papers from the 5th International Conference on Building Energy and Environment (COBEE2022), held in Montreal, Canada, in July 2022. The work focuses on the most recent technologies and knowledge of building energy and the environment, including health, energy, urban microclimate, smart cities, safety, etc. The contents make valuable contributions to academic researchers, engineers in the industry, and regulators of buildings. As well, readers encounter new ideas for achieving healthy, comfortable, energy-efficient, resilient, and safe buildings.
Author: Divas Grover
Publisher:
ISBN:
Category :
Languages : en
Pages : 56
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Book Description
Sustainability and reducing energy consumption are targets for building operations. The installation of smart sensors and Building Automation Systems (BAS) makes it possible to study facility operations under different circumstances. These technologies generate large amounts of data. That data can be scrapped and used for the analysis. In this thesis, we focus on the process of data-driven modeling and decision making from scraping the data to simulate the building and optimizing the operation. The City of Orlando has similar goals of sustainability and reduction of energy consumption so, they provided us access to their BAS for the data and study the operation of its facilities. The data scraped from the City's BAS serves can be used to develop statistical/machine learning methods for decision making. We selected a mid-size pilot building to apply these techniques. The process begins with the collection of data from BAS. An Application Programming Interface (API) is developed to login to the servers and scrape data for all data points and store it on the local machine. Then data is cleaned to analyze and model. The dataset contains various data points ranging from indoor and outdoor temperature to fan speed inside the Air Handling Unit (AHU) which are operated by Variable Frequency Drive (VFD). This whole dataset is a time series and is handled accordingly. The cleaned dataset is analyzed to find different patterns and investigate relations between different data points. The analysis helps us in choosing parameters for models that are developed in the next step. Different statistical models are developed to simulate building and equipment behavior. Finally, the models along with the data are used to optimize the building Operation with the equipment constraints to make decisions for building operation which leads to a reduction in energy consumption while maintaining temperature and pressure inside the building.
Author: Srinivasan Iyengar
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Buildings account for over 40% of the energy and 75% of the electricity usage. Thus, by reducing our energy footprint in buildings, we can improve our overall energysustainability. Further, the proliferation of networked sensors and IoT devices in recent years have enabled monitoring of buildings to provide data at various granularity. For example, smart plugs monitor appliance level usage inside the house, while solar meters monitor residential rooftop solar installations. Furthermore, smart meters record energy usage at a grid-scale. In this thesis, I argue that data-driven modeling applied to the IoT data from a smart building, at varying granularity, in association with third party data can help to understand and reduce human energy consumption. I present four data-driven modeling approaches - that use sophisticated techniques from Machine Learning, Optimization, and Time Series Analysis - applied at different granularities. First, I study IoT devices inside the house and discuss an approach called NIMD that automatically models individual electrical loads found in a household. The analytical model resulting from this approach can be used in several applications. For example, these models can improve the performance of NILM algorithms to disaggregate loads in a given household. Further, faulty or energy-inefficient appliances can be identified by observing deviations in model parameters over its lifetime. Second, I examine data from solar meters and present a machine learning framework called SolarCast to forecast energy generation from residential rooftop installations. The predictions enable exploiting the benefits of locally-generated solar energy. Third, I employ a sensorless approach utilizing a graphical model representation to report city-scale photovoltaic panel health and identify anomalies in solar energy production. Immediate identification of faults maximizes the solar investment by aiding in optimal operational performance. Finally, I focus on grid-level smart meter data and use correlations between energy usage and external weather to derive probabilistic estimates of energy, which is leveraged to identify the least efficient buildings from a large population along with the underlying cause of energy inefficiency. The identified homes can be targeted for custom energy efficiency programs.
Author: B Rajanarayan Prusty
Publisher: CRC Press
ISBN: 1040016111
Category : Technology & Engineering
Languages : en
Pages : 253
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Book Description
This book provides a comprehensive understanding of how intelligent data-driven techniques can be used for modelling, controlling, and optimizing various power and energy applications. It aims to develop multiple data-driven models for forecasting renewable energy sources and to interpret the benefits of these techniques in line with first-principles modelling approaches. By doing so, the book aims to stimulate deep insights into computational intelligence approaches in data-driven models and to promote their potential applications in the power and energy sectors. Its key features include: an exclusive section on essential preprocessing approaches for the data-driven model a detailed overview of data-driven model applications to power system planning and operational activities specific focus on developing forecasting models for renewable generations such as solar PV and wind power, and showcasing the judicious amalgamation of allied mathematical treatments such as optimization and fractional calculus in data-driven model-based frameworks This book presents novel concepts for applying data-driven models, mainly in the power and energy sectors, and is intended for graduate students, industry professionals, research, and academic personnel.
Author: Ahmed Hammami
Publisher: Springer Nature
ISBN: 3030855848
Category : Technology & Engineering
Languages : en
Pages : 270
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Book Description
This book provides readers with an overview of recent theories and methods for studying complex mechanical systems used in energy production, such as wind turbines, but not limited to them. The emphasis is put on strategies for increasing energy efficiency, and on recent industrial applications. Topics cover dynamics and vibration, vibroacoustics, engineering design, modelling and simulation, fault diagnostics, signal processing and prognostics. The book is based on peer-review contributions and invited talks presented at the first International Workshop on MOdelling and Simulation of COmplex Systems for Sustainable Energy Efficiency, MOSCOSSEE 2021, held online on February 25-26, 2021, and organized by the LAboratory of Mechanics, Modelling and Production (LA2MP) from University of Sfax, Tunisia and the Department of Mechanical and Aeronautical engineering, Centre of Asset Integrity Management (C-AIM) from University of Pretoria, South Africa. By offering authoritative information on innovative methods and tools for application in renewable energy production, it provides a valuable resource to both academics and professionals, and a bridge to facilitate communication between the two groups.
Author: Andreas Kämper
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Author: Zhiqiang Geng
Publisher: Frontiers Media SA
ISBN: 2832535763
Category : Technology & Engineering
Languages : en
Pages : 153
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Book Description
