Artificial Neural Networks to assess energy and environmental performance of buildings: An Italian case study

D’Amico, A.; Ciulla, Giuseppina; Traverso, Marzia; Brano, Valerio Lo; Palumbo, Elisabetta

doi:10.1016/j.jclepro.2019.117993

Cited by 69 publications

(29 citation statements)

References 47 publications

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“…In this way, investing in the energy requalification of school buildings could have great economic and social impacts, resulting in significant savings for the public administration budget and becoming a prime mover for job creation [40].…”

Section: Italian Energy Contextmentioning

confidence: 99%

Multi-Energy School System for Seasonal Use in the Mediterranean Area

et al. 2020

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School buildings represent an energy-consuming sector of real estate where different efficiency actions are necessary. The literature shows how the design of a multi-energy system offers numerous advantages, however, there are problems related to the integration of cogeneration units with renewable energy sources due to the low flexibility of the first one and the high degree of uncertainty of the latter. The authors provide an alternative solution through the analysis of a case study consisting of a multiple energy system in three Sicilian schools, focusing on the system’s operational strategy, on the design and sizing of components and trying to exploit the energy needs complementarity of buildings instead of integrating the conventional energy storage systems. Not considering school activities in summer, it was decided to install a cogeneration unit sized on winter thermal loads, whereas the electricity demand not covered was reduced with photovoltaic systems designed to maximize production for seasonal use and with loads concentrated in the morning hours. The effectiveness of this idea, which can be replicated for similar users and areas, is proved by a payback time of less than 11 years and a reduction of 31.77% of the CO2 emissions.

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Section: Italian Energy Contextmentioning

confidence: 99%

Multi-Energy School System for Seasonal Use in the Mediterranean Area

et al. 2020

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“…The artificial neural network (ANN) is a multi-dimensional information space that can learn information patterns. ANN exhibits strong computation intelligence, which can predict any complex and non-linear system [117]- [119] ANN has the advantage to manage and control several types of problems with its improved learning ability and without depending on the mathematical functional relationship [120], [121]. ANN is employed in building energy management scheduling controller, [122], as shown in Fig.…”

Section: ) Artificial Neural Network Controlmentioning

confidence: 99%

Intelligent Controllers and Optimization Algorithms for Building Energy Management Towards Achieving Sustainable Development: Challenges and Prospects

et al. 2021

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Buildings account for a significant amount of energy consumption leading to the issues of global emissions and climate change. Thus, energy management in a building is increasingly explored due to its significant potential in reducing the overall electricity expenses for the consumers and mitigating carbon emissions. In line with that, the greater control and optimization of energy management integrated with renewable energy resources is required to improve building energy efficiency while satisfying indoor environment comfort. Even though actions are being taken to reduce the energy consumption in buildings with several optimization and controller techniques, yet some issues remain unsolved. Therefore, this work provides a comprehensive review of the conventional and intelligent control methods with emphasis on their classification, features, configuration, benefits, and drawbacks. This review critically investigates the different optimization objectives and constraints with respect to comfort management, energy consumption, and scheduling. Furthermore, the review outlines the different methodological approaches to optimization algorithms used in building energy management. The contributions of controller and optimization in building energy management with the relation of sustainable development goals (SDGs) are explained rigorously. Discussions on the key challenges of the existing methods are presented to identify the gaps for future research. The review delivers some effective future directions that would be beneficial to the researchers and industrialists to design an efficiently optimized controller for building energy management toward targeting SDGs.INDEX TERMS Building energy management, controller, optimization, scheduling, sustainable development goals I. INTRODUCTIONPresently, buildings take the lead in consuming a substantial amount of energy, indicating about 40% of global energy consumption, which is responsible to release one-third of greenhouse gas (GHG) emissions [1], [2]. Another report demonstrates that buildings hold 49% of the total energy worldwide in which 60% of the energy is consumed for heating and cooling purposes [3], [4]. The poor management and ineffective control approach of appliances used in the building may result in a significant loss of energy in a

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“…It consists of 29 inputs and generates seven outputs, including heating demand, and six environmental impacts categories. This model can be utilised by nonexpert users in LCA or building energy modelling (Amico et al, 2019). Another study developed a model that links Simulation-Based Multi-Objective Optimization (SBMO) with ANN.…”

Section: Previous Studiesmentioning

confidence: 99%

Environmental impacts cost assessment model of residential building using an artificial neural network

Hamida

Al‐Sudairi

Alshaibani

et al. 2020

ECAM

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PurposeBuildings are major contributors to greenhouse gases (GHG) along the various stages of the building life cycle. A range of tools have been utilised for estimating building energy use and environmental impacts; these are time-consuming and require massive data that are not necessarily available during early design stages. Therefore, this study aimed to develop an Environmental Impacts Cost Assessment Model (EICAM) that quantifies both energy and environmental costs for residential buildings.Design/methodology/approachAn Artificial Neural Network (ANN) was employed to develop the EICAM. The model consists of six input parameters, including wall type, roof type, glazing type, window to wall ratio (WWR), shading device and building orientation. In addition, the model calculates four measures: annual energy cost, operational carbon over 20 years, envelope embodied carbon and total carbon per square metre. The ANN architecture is 6:13:4:4, where the conjugate gradient algorithm was applied to train the model and minimise the mean squared error (MSE). Furthermore, regression analysis for the ANN prediction for each output was performed.FindingsThe MSE was minimised to 0.016 while training the model. Also, the correlation between each ANN output and the actual output was very strong, with an R2 value for each output of almost 0.998. Moreover, validation was conducted for each output, with the error percentages calculated at 0.26%, 0.25%, 0.03% and 0.27% for the annual energy cost, operational carbon, envelope materials embodied carbon and total carbon per square metre, respectively. Accordingly, the EICAM contributes to enhancing design decision-making concerning energy consumption and carbon emissions in the early design stages.Research limitations/implicationsThis study provides theoretical implications to the domain of building environmental impact assessment through illustrating a systematic approach for developing an energy-based prediction model that generates four environmental-oriented outputs, namely energy cost, operational energy carbon, envelope embodied carbon, and total carbon. The model developed has practical implications for the architectural/engineering (A/E) industries by providing a useful tool to easily predict environmental impact costs during the early design phase. This would enable designers in Saudi Arabia to make effective design decisions that would increase sustainability in the building life cycle.Originality/valueBy providing a holistic predictive model entitled EICAM, this study endeavours to bridge the gap between energy costs and environmental impacts in a predictive model for Saudi residential units. The novelty of this model is that it is an alternative tool that quantifies both energy cost, as well as building’s environmental impact, in one model by using a machine learning approach. Besides, EICAM predicts its outcomes more quickly than conventional tools such as DesignBuilder and is reliable for predicting accurate environmental impact costs during early design stages.

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Artificial Neural Networks to assess energy and environmental performance of buildings: An Italian case study

Cited by 69 publications

References 47 publications

Multi-Energy School System for Seasonal Use in the Mediterranean Area

Multi-Energy School System for Seasonal Use in the Mediterranean Area

Intelligent Controllers and Optimization Algorithms for Building Energy Management Towards Achieving Sustainable Development: Challenges and Prospects

Environmental impacts cost assessment model of residential building using an artificial neural network

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