Prediction and optimization of energy consumption in an office building using artificial neural network and a genetic algorithm

Sustainable Cities and Society, Journal Year: 2020, Volume and Issue: 61, P. 102325 - 102325

Published: June 12, 2020

Language: Английский

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Machine Learning and Deep Learning in Energy Systems: A Review

Sustainability, Journal Year: 2022, Volume and Issue: 14(8), P. 4832 - 4832

Published: April 18, 2022

With population increases and a vital need for energy, energy systems play an important decisive role in all of the sectors society. To accelerate process improve methods responding to this increase demand, use models algorithms based on artificial intelligence has become common mandatory. In present study, comprehensive detailed study been conducted applications Machine Learning (ML) Deep (DL), which are newest most practical Artificial Intelligence (AI) systems. It should be noted that due development DL algorithms, usually more accurate less error, these ability model solve complex problems field. article, we have tried examine very powerful problem solving but received attention other studies, such as RNN, ANFIS, RBN, DBN, WNN, so on. This research uses knowledge discovery databases understand ML systems’ current status future. Subsequently, critical areas gaps identified. addition, covers efficient used field; optimization, forecasting, fault detection, investigated. Attempts also made cover their evaluation metrics, including not only important, newer ones attention.

Language: Английский

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Modelling heating and cooling energy demand for building stock using a hybrid approach

Energy and Buildings, Journal Year: 2021, Volume and Issue: 235, P. 110740 - 110740

Published: Jan. 14, 2021

Language: Английский

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Advances in emerging digital technologies for energy efficiency and energy integration in smart cities

Energy and Buildings, Journal Year: 2024, Volume and Issue: 315, P. 114289 - 114289

Published: May 17, 2024

Language: Английский

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Pattern-driven behaviour for demand-side management: An analysis of appliance use

Energy and Buildings, Journal Year: 2024, Volume and Issue: 308, P. 113988 - 113988

Published: Feb. 15, 2024

Energy communities play a key role in the transition to sustainable energy, helping inform and engage end-users so that they can become active energy consumers. In practice, trials pilots often risk failure due misplaced expectations unforeseen behaviours when it comes achieving flexible demand resources. order tackle these challenges, residential electricity load profile datasets consumer survey results emerge as powerful tools for identifying controllable loads, consumption models, tailored understanding of communities' contexts. This paper first outlines analyses datasets' capabilities leverage data-driven decision-making more efficient deployments demand-side management (DSM) systems. A number appliance behaviour patterns are extracted, based on high loads shifting, being validated over three different use cases support turn-key DSM presence absence renewable supply bill saving. genetic algorithm optimization is applied underpin reallocation optimal community profiles by combining time-variable tariff use. Experiments demonstrate shiftable appliances reduce average peak up 29% increasing self-consumption, leading valuable saving 9%. Our findings also point current limitations existing load/consumption datasets, which hindering design flexibility response programmes communities.

Language: Английский

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Applying machine learning approach in recycling

Journal of Material Cycles and Waste Management, Journal Year: 2021, Volume and Issue: 23(3), P. 855 - 871

Published: Feb. 17, 2021

Language: Английский

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Energy Auditing for Efficient Planning and Implementation in Commercial and Residential Buildings

Advances in Civil Engineering, Journal Year: 2021, Volume and Issue: 2021(1)

Published: Jan. 1, 2021

The ideology of ensuring energy‐efficient design and construction buildings by providing minimum requirements is the core objective this work. Energy audit was conducted to improve building with incremental further enhance energy efficiency. Conservation Building Code (ECBC) has been modified extensively over years, starting from its initial deployment in year 2011 latest modifications 2019. conservation standards ECBC apply envelope, heating ventilation, air conditioning, lighting, service water heating, electric power distribution. It should also be ensured that all‐electric systems, transformers, motors, diesel generators must meet regulated set mandatory requirements. From among various software types have approved for application, study employed Plus simulate based on given input selected location. location chosen Bhubaneshwar, India. All necessary details ranging latitude, longitude, weather, time zone, elevation, area, cooling, much more covered simulation. Utilizing an resulted increase savings 27.4%, thus, qualifies regarded as compliant building.

Language: Английский

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Analysis of feature matrix in machine learning algorithms to predict energy consumption of public buildings

Energy and Buildings, Journal Year: 2021, Volume and Issue: 249, P. 111208 - 111208

Published: June 24, 2021

Language: Английский

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Predicting energy cost of public buildings by artificial neural networks, CART, and random forest

Neurocomputing, Journal Year: 2021, Volume and Issue: 439, P. 223 - 233

Published: Feb. 10, 2021

Language: Английский

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Machine Learning-Based Microclimate Model for Indoor Air Temperature and Relative Humidity Prediction in a Swine Building

Animals, Journal Year: 2021, Volume and Issue: 11(1), P. 222 - 222

Published: Jan. 18, 2021

Indoor air temperature (IAT) and indoor relative humidity (IRH) are the prominent microclimatic variables; still, potential contributors that influence homeostasis of livestock animals reared in closed barns. Further, predicting IAT IRH encourages farmers to think ahead actively prepare optimum solutions. Therefore, primary objective current literature is build investigate extensive performance analysis between popular ML models practice used for predictions. Meanwhile, multiple linear regression (MLR), multilayered perceptron (MLP), random forest (RFR), decision tree (DTR), support vector (SVR) were utilized prediction. This study accessible factors such as external environmental data simulate models. In addition, three different input datasets named S1, S2, S3 assess From results, RFR performed better results both (R2 = 0.9913; RMSE 0.476; MAE 0.3535) 0.9594; 2.429; 1.47) prediction among other particularly with datasets. it has been proven selecting right features from given builds supportive conditions under which expected available. Overall, demonstrates a model predict naturally ventilated swine building containing fewer attributes.

Language: Английский

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