An Overview of Artificial Intelligence-Based Methods for Building Energy Systems

2003 ◽  
Vol 125 (3) ◽  
pp. 331-342 ◽  
Author(s):  
Moncef Krarti
Keyword(s):  
Heat Transfer ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Energy Use ◽  
Energy Systems ◽  
Building Energy ◽  
Building Envelope ◽  
Building Energy Systems ◽  
Intelligence Approach ◽  
Artificial Intelligence Approach

An overview of commonly used methodologies based on the artificial intelligence approach is provided with a special emphasis on neural networks, fuzzy logic, and genetic algorithms. A description of selected applications to building energy systems of AI approaches is outlined. In particular, methods using the artificial intelligence approach for the following applications are discussed: Prediction energy use for one building or a set of buildings (served by one utility), Modeling of building envelope heat transfer, Controlling central plants in buildings, and Fault detection and diagnostics for building energy systems.

scholarly journals Combined Building-Energy Systems with Heat Transfer Control by Building Constructions using RES

2021 ◽  
Vol 1203 (3) ◽  
pp. 032091
Author(s):  
Daniel Kalús ◽  
Martin Cvíčela ◽  
Peter Janík ◽  
Matej Kubica
Keyword(s):  
Heat Transfer ◽  
Civil Engineering ◽  
Thermal Protection ◽  
Energy Systems ◽  
Building Energy ◽  
Experimental Measurements ◽  
Building Structures ◽  
Mobile Laboratory ◽  
Systems Research ◽  
Building Energy Systems

Abstract Energy systems built into one of the building structures that serve to capture solar energy, geothermic energy, and ambient energy, or which have the function of end elements of heating, cooling, and ventilation system, we generally call combined building-energy systems. Among combined building-energy systems we include solar roofs with built-in pipe absorbers, building structures with active thermal protection (ATP) - active heat transfer control, which have a multifunctional purpose – a thermal barrier, low-temperature heating, high-temperature cooling, recuperation and accumulation of heat, solar and ambient energy collection, large-capacity heat storage (ground heat accumulators built simultaneously in the foundation slab of the building), or heat exchangers used for recuperative ventilation of buildings built into the foundation slabs and wall structures. The research of combined building-energy systems at the Department of Building Services, Faculty of Civil Engineering, Slovak University of Technology in Bratislava has been carried out continuously since 2005. Within five research projects (responsible researcher, Kalús, D.) HZ 04-309-05, HZ 04-310- 05, HZ 04-142-07 (research and experimental measurements took place in the years 2005 to 2007), HZ PG73/2011 (research and experimental measurements took place in the years 2011 to 2013), [13,] and HZ PR10/2015 (research and experimental measurements have been carried out since 2015), two experimental houses IDA I. and EB2020, a mobile laboratory designed for measuring and optimizing a compact heat station using renewable heat sources, were designed and built by the research team at our workplace, and also a research of a fragment of a perimeter wall with built-in active thermal protection was carried out in the climatic chamber of the Faculty of Civil Engineering STU in Bratislava, Slovak Republic. Significant contribution to the research was provided by doctoral students Ing. Martin Cvíčela, Ph.D., (supervisor, Kalús, D.), Ing. Peter Janik, PhD., (supervisor, Kalús, D.) and Ing. Martin Šimko, PhD., (supervisor, Kalús, D.), who described the results of the research in their dissertations. At present experimental measurements in the mobile laboratory are performed by doctoral student Ing. Matej Kubica, (supervisor, Kalús, D.). In the area of combined construction and energy systems, research and optimization of suitable solutions continues, which have been transformed into one European patent and three utility models.

scholarly journals Evolutionary Integrated Heuristic with Gudermannian Neural Networks for Second Kind of Lane–Emden Nonlinear Singular Models

2021 ◽  
Vol 11 (11) ◽  
pp. 4725
Author(s):  
Kashif Nisar ◽  
Zulqurnain Sabir ◽  
Muhammad Asif Zahoor Raja ◽  
Ag. Asri Ag. Ibrahim ◽  
Joel J. P. C. Rodrigues ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Active Set ◽  
Active Set Method ◽  
Hybrid Heuristics ◽  
Hidden Layer ◽  
The Stability ◽  
Intelligence Approach ◽  
Artificial Intelligence Approach ◽  
Search Capability

In this work, a new heuristic computing design is presented with an artificial intelligence approach to exploit the models with feed-forward (FF) Gudermannian neural networks (GNN) accomplished with global search capability of genetic algorithms (GA) combined with local convergence aptitude of active-set method (ASM), i.e., FF-GNN-GAASM to solve the second kind of Lane–Emden nonlinear singular models (LE-NSM). The proposed method based on the computing intelligent Gudermannian kernel is incorporated with the hidden layer configuration of FF-GNN models of differential operatives of the LE-NSM, which are arbitrarily associated with presenting an error-based objective function that is used to optimize by the hybrid heuristics of GAASM. Three LE-NSM-based examples are numerically solved to authenticate the effectiveness, accurateness, and efficiency of the suggested FF-GNN-GAASM. The reliability of the scheme via statistical valuations is verified in order to authenticate the stability, accuracy, and convergence.

Continuous CommissioningSM of Building Energy Systems

2003 ◽  
Vol 125 (3) ◽  
pp. 275-281 ◽  
Author(s):  
Mingsheng Liu ◽  
David E. Claridge ◽  
W. D. Turner
Keyword(s):  
Energy Use ◽  
Energy Systems ◽  
System Optimization ◽  
Building Energy ◽  
Scientific Field ◽  
System Control ◽  
Global System ◽  
Building Energy Systems ◽  
Integrated Approaches

Continuous Commissioning (CCSM) is an ongoing process to resolve operating problems, improve comfort, optimize energy use, and identify retrofits for existing commercial and institutional buildings and central plant facilities. CC focuses on optimizing/improving overall system control and operations for the building as it is currently utilized and on meeting existing facility needs. Innovative optimal engineering solutions are developed using engineering-based model analysis integrated with scientific field measurement. Integrated approaches are used to implement these solutions to ensure practical local and global system optimization and to ensure persistence of the improved operational schedules. Implementation of the CC process has typically decreased building energy consumption by 20% in well over 100 large buildings where it has been implemented. This paper presents the CC process, the primary CC techniques and measures, and a case study.

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