Climate responsive cooling control using artificial neural networks

Venkatesan, K.G.S.; Ramachandraiah, Uppu

doi:10.1016/j.jobe.2018.05.008

Cited by 12 publications

(4 citation statements)

References 18 publications

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“…However, the idea to generate solutions to given problems based on data has been broadly investigated. For example, ANNs, a resurgent and prominent method for predictive analytics, has been used to aid in the design of water harvesting structures (Chandwani et al, 2016), predicting and controlling cooling loads in buildings (Venkatesan and Ramachandraiah, 2018), predicting risks for building maintenance (de Silva et al, 2013), and predicting the escalation of highway construction cost over time (Wilmot and Mei, 2005). Generative and Genetic Algorithms (GAs) have been used to predict structural designs solutions given limited spatial data (Davila Delgado et al 2013;Hofmeyer et al, 2013;.…”

Section: Predictive Analyticsmentioning

confidence: 99%

Big Data Analytics System for Costing Power Transmission Projects

Delgado

Bilal

Ajayi

et al. 2020

J. Constr. Eng. Manage.

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Inaccurate cost estimates have significant impacts on the final cost of power transmission projects and erode profits. Methods for cost estimation have been investigated thoroughly, but they are not used widely in practice. The purpose of this study is to leverage a Big Data architecture, to manage the large and diverse data required for predictive analytics. This paper presents a Predictive Analytics and Modelling System (PAMS) that facilitates the use of different data-driven cost prediction methods. A 2.75 million-point dataset of power transmission projects has been used as a case study. The proposed Big Data architecture is fit for purpose. It can handle the diverse datasets used in the construction sector. The three most prevalent cost estimation models were implemented (linear regression, support vector regression, and artificial neural networks). All models performed better than the estimated human-level performance. The primary contribution of this study to the Body of Knowledge is an empirical indication that data-driven methods analysed in this study are on average 13.5% better than manual methods for cost estimation of power transmission projects. Additionally, the paper presents a Big Data architecture that can manage and process large varied datasets and seamless scalability.

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Section: Predictive Analyticsmentioning

confidence: 99%

Big Data Analytics System for Costing Power Transmission Projects

Delgado

Bilal

Ajayi

et al. 2020

J. Constr. Eng. Manage.

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“…Avoiding incompatibility problems comes from doing the modelling in the Simulink environment. To utilize an arti cial neural network (ANN) to simulate the model predictive control of the HVAC system, Venkatesan et al [12] established a heat ow model and equivalent thermal model from EnergyPlus. Alibabaei et al [13] used a TRNSYS-Matlab cosimulator with an advance control strategy to study the e ciency of di erent strategy models.…”

Section: Introductionmentioning

confidence: 99%

Development of a Coupled EnergyPlus-MATLAB Simulation Based on LSTM for Predictive Control of HVAC System

Cong

Hou

et al. 2022

Mathematical Problems in Engineering

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To avoid unnecessary energy waste due to the single temperature setpoints of the heating, ventilation, and air conditioning (HVAC) system during the seasonal variation period, this study proposed a long-short-term memory (LSTM) neural network to predict and control the temperature setpoint. The thermal comfort, cooling rate, and heating rate were predicted with outdoor environment parameters and occupant count. A large scale of operation data was collected from the EnergyPlus simulation, which was previously developed based on the characteristics of a real case study house. Different kinds of input characteristics were offered to test the stable use of LSTM and other artificial neural networks. This paper discusses the development of a Matlab EnergyPlus co-simulation to predict and control the temperature setpoints of a variable air volume system, especially the relationship between temperature setpoint and energy consumption. The simulation results indicate the advantages of the LSTM prediction of energy consumption and the potential for energy saving with predictive control.

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“…Improved dwelling design has shown to reduce energy consumption as well as providing safer and more comfortable indoor environments. Current studies recommend a combination of bio-climatic design features, such as optimizing the orientation, size of glazing, effective ventilation, solar shading [9][10][11], combined with passive heating and cooling [12,13]. New architectural approaches [14] and supporting regulations are vital for an improved energy efficiency across the residential sector.…”

Section: Introductionmentioning

confidence: 99%

Affordable, Energy-Efficient Housing Design for Chile: Achieving Passivhaus Standard with the Chilean State Housing Subsidy

et al. 2020

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In Chile, it is estimated that the energy demand will continue to increase if substantial energy efficiency measures in housing are not taken. These measures are generally associated with technical and mainly economic difficulties. This paper aims to show the technical and economic feasibility of achieving Passivhaus standard house in Chile, considering the budget of the maximum state subsidy currently available (Chilean Unidad de Fomento (CLF) 2000 ≈ 81,000 USD). The design was simulated in the Passive House Planning Package software to determine if the house could be certified with the selected standard. At the same time, the value of all the items was quantified in order not to exceed the stipulated maximum budget for a house considered as affordable. It was shown that in terms of design it is possible to implement the Passivhaus standard given the current housing subsidy. The designed housing ensures a reduction of 85% in heating demand and a 60% reduction in CO2 emissions during the operation, compared to an average typical Chilean house.

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Climate responsive cooling control using artificial neural networks

Cited by 12 publications

References 18 publications

Big Data Analytics System for Costing Power Transmission Projects

Big Data Analytics System for Costing Power Transmission Projects

Development of a Coupled EnergyPlus-MATLAB Simulation Based on LSTM for Predictive Control of HVAC System

Affordable, Energy-Efficient Housing Design for Chile: Achieving Passivhaus Standard with the Chilean State Housing Subsidy

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