Development of an IoT-based big data platform for day-ahead prediction of building heating and cooling demands

Luo, X. J.; Oyedele, Lukumon O.; Ajayi, Anuoluwapo O.; Monyei, Chukwuka G.; Akinade, Olugbenga O.; Akanbi, Lukman A.

doi:10.1016/j.aei.2019.100926

A deep learning approach to concrete water-cement ratio prediction (2022)
Journal Article
Oyedele, L., Bello, S., Olaitan, O. K., Olonade, K. A., Olajumoke, A. M., Ajayi, A., …Bello, A. L. (2022). A deep learning approach to concrete water-cement ratio prediction. Results in Materials, 15(September 2022), Article 100300. https://doi.org/10.1016/j.rinma.2022.100300

Concrete is a versatile construction material, but the water content can greatly influence its quality. However, using the trials and error method to determine the optimum water for the concrete mix results in poor quality concrete structures, which... Read More about A deep learning approach to concrete water-cement ratio prediction.

Deep learning and boosted trees for injuries prediction in power infrastructure projects (2021)
Journal Article
Oyedele, A., Ajayi, A., Oyedele, L. O., Delgado, J. M. D., Akanbi, L., Akinade, O., …Bilal, M. (2021). Deep learning and boosted trees for injuries prediction in power infrastructure projects. Applied Soft Computing, 110(107587), 1 - 14. https://doi.org/10.1016/j.asoc.2021.107587

Electrical injury impacts are substantial and massive. Investments in electricity will continue to increase, leading to construction project complexities, which undoubtedly contribute to injuries and associated effects. Machine learning (ML) algorith... Read More about Deep learning and boosted trees for injuries prediction in power infrastructure projects.

IoT technologies for livestock management: A review of present status, opportunities, and future trends (2021)
Journal Article
Akhigbe, B. I., Munir, K., Akinade, O., Akanbi, L., & Oyedele, L. O. (2021). IoT technologies for livestock management: A review of present status, opportunities, and future trends. Big Data and Cognitive Computing, 5(1), Article 10. https://doi.org/10.3390/bdcc5010010

The world population currently stands at about 7 billion amidst an expected increase in 2030 from 9.4 billion to around 10 billion in 2050. This burgeoning population has continued to influence the upward demand for animal food. Moreover, the managem... Read More about IoT technologies for livestock management: A review of present status, opportunities, and future trends.

Cloud computing in construction industry: Use cases, benefits and challenges (2020)
Journal Article
Bello, S. A., Oyedele, L. O., Akinade, O. O., Bilal, M., Davila Delgado, J. M., Akanbi, L. A., …Owolabi, H. A. (2021). Cloud computing in construction industry: Use cases, benefits and challenges. Automation in Construction, 122, Article 103441. https://doi.org/10.1016/j.autcon.2020.103441

Cloud computing technologies have revolutionised several industries (such as aerospace, manufacturing, automobile, retail, etc.) for several years. Although the construction industry is well placed to also leverage these technologies for competitive... Read More about Cloud computing in construction industry: Use cases, benefits and challenges.

BIM competencies for delivering waste-efficient building projects in a circular economy (2020)
Journal Article
Ganiyu, S. A., Oyedele, L. O., Akinade, O., Owolabi, H., Akanbi, L., & Gbadamosi, A. (2020). BIM competencies for delivering waste-efficient building projects in a circular economy. Developments in the Built Environment, 4, 100036. https://doi.org/10.1016/j.dibe.2020.100036

Competency measures are increasingly becoming effective ways for construction organizations to measure their ability to deliver waste-efficient projects. Despite the ongoing efforts in achieving the goals of the circular economy through BIM adoption,... Read More about BIM competencies for delivering waste-efficient building projects in a circular economy.

Deep learning in the construction industry: A review of present status and future innovations (2020)
Journal Article
Akinosho, T. D., Oyedele, L. O., Bilal, M., Ajayi, A. O., Delgado, M. D., Akinade, O. O., & Ahmed, A. A. (2020). Deep learning in the construction industry: A review of present status and future innovations. Journal of Building Engineering, 32, Article 101827. https://doi.org/10.1016/j.jobe.2020.101827

The construction industry is known to be overwhelmed with resource planning, risk management and logistic challenges which often result in design defects, project delivery delays, cost overruns and contractual disputes. These challenges have instigat... Read More about Deep learning in the construction industry: A review of present status and future innovations.

Life cycle assessment approach for renewable multi-energy system: A comprehensive analysis (2020)
Journal Article
Luo, X., Oyedele, L. O., Owolabi, H. A., Bilal, M., Ajayi, A. O., & Akinade, O. O. (2020). Life cycle assessment approach for renewable multi-energy system: A comprehensive analysis. Energy Conversion and Management, 224, Article 113354. https://doi.org/10.1016/j.enconman.2020.113354

In response to the gradual degradation of natural sources, there is a growing interest in adopting renewable resources for various building energy supply. In this study, a comprehensive life cycle assessment approach is proposed for a renewable multi... Read More about Life cycle assessment approach for renewable multi-energy system: A comprehensive analysis.

Genetic algorithm-determined deep feedforward neural network architecture for predicting electricity consumption in real buildings (2020)
Journal Article
Luo, X. J., Oyedele, L. O., Ajayi, A. O., Akinade, O. O., Delgado, J. M. D., Owolabi, H. A., & Ahmed, A. (2020). Genetic algorithm-determined deep feedforward neural network architecture for predicting electricity consumption in real buildings. Energy and AI, 2, Article 100015. https://doi.org/10.1016/j.egyai.2020.100015

A genetic algorithm-determined deep feedforward neural network architecture (GA-DFNN) is proposed for both day-ahead hourly and week-ahead daily electricity consumption of a real-world campus building in the United Kingdom. Due to the comprehensive r... Read More about Genetic algorithm-determined deep feedforward neural network architecture for predicting electricity consumption in real buildings.

Comparative study of machine learning-based multi-objective prediction framework for multiple building energy loads (2020)
Journal Article
Luo, X. J., Oyedele, L. O., Ajayi, A. O., & Akinade, O. O. (2020). Comparative study of machine learning-based multi-objective prediction framework for multiple building energy loads. Sustainable Cities and Society, 61, Article 102283. https://doi.org/10.1016/j.scs.2020.102283

Buildings are one of the significant sources of energy consumption and greenhouse gas emission in urban areas all over the world. Lighting control and building integrated photovoltaic (BIPV) are two effective measures in reducing overall primary ener... Read More about Comparative study of machine learning-based multi-objective prediction framework for multiple building energy loads.

Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings (2020)
Journal Article
Luo, X. J., Oyedele, L. O., Ajayi, A. O., Akinade, O. O., Owolabi, H. A., & Ahmed, A. (2020). Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings. Renewable and Sustainable Energy Reviews, 131, Article 109980. https://doi.org/10.1016/j.rser.2020.109980

Accurate forecast of energy consumption is essential in building energy management. Owing to the variation of outdoor weather condition among different seasons, year-round historical weather profile is needed to investigate its feature thoroughly. Da... Read More about Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings.

Two-stage capacity optimization approach of multi-energy system considering its optimal operation (2020)
Journal Article
Luo, X. J., Oyedele, L. O., Akinade, O. O., & Ajayi, A. O. (2020). Two-stage capacity optimization approach of multi-energy system considering its optimal operation. Energy and AI, 1, Article 100005. https://doi.org/10.1016/j.egyai.2020.100005

With the depletion of fossil fuel and climate change, multi-energy systems have attracted widespread attention in buildings. Multi-energy systems, fuelled by renewable energy, including solar and biomass energy, are gaining increasing adoption in com... Read More about Two-stage capacity optimization approach of multi-energy system considering its optimal operation.

3D pattern identification approach for cooling load profiles in different buildings (2020)
Journal Article
Luo, X. J., Oyedele, L. O., Akinade, O., & Ajayi, A. O. (2020). 3D pattern identification approach for cooling load profiles in different buildings. Journal of Building Engineering, 31, Article 101339. https://doi.org/10.1016/j.jobe.2020.101339

© 2020 Elsevier Ltd Building energy conservation has gained increasing concern owing to its large portion of energy consumption and great potential of energy saving. In-depth understanding of representative patterns of daily cooling load profile will... Read More about 3D pattern identification approach for cooling load profiles in different buildings.

Optimised Big Data analytics for health and safety hazards prediction in power infrastructure operations (2020)
Journal Article
Ajayi, A., Oyedele, L., Akinade, O., Bilal, M., Owolabi, H., Akanbi, L., & Delgado, J. M. D. (2020). Optimised Big Data analytics for health and safety hazards prediction in power infrastructure operations. Safety Science, 125, Article 104656. https://doi.org/10.1016/j.ssci.2020.104656

© 2020 Elsevier Ltd Forecasting imminent accidents in power infrastructure projects require a robust and accurate prediction model to trigger a proactive strategy for risk mitigation. Unfortunately, getting ready-made machine learning algorithms to e... Read More about Optimised Big Data analytics for health and safety hazards prediction in power infrastructure operations.

Risk mitigation in PFI/PPP project finance: A framework model for financiers’ bankability criteria (2019)
Journal Article
Owolabi, H., Oyedele, L., Alaka, H., Ajayi, S., Bilal, M., & Akinade, O. (2020). Risk mitigation in PFI/PPP project finance: A framework model for financiers’ bankability criteria. Built Environment Project and Asset Management, 10(1), 28-49. https://doi.org/10.1108/BEPAM-09-2018-0120

Purpose: Earlier studies on risk evaluation in private finance initiative and public private partnerships (PFI/PPP) projects have focussed more on quantitative approaches despite increasing call for contextual understanding of the bankability of risk... Read More about Risk mitigation in PFI/PPP project finance: A framework model for financiers’ bankability criteria.

Deep learning models for health and safety risk prediction in power infrastructure projects (2019)
Journal Article
Ajayi, A., Oyedele, L., Owolabi, H., Akinade, O., Bilal, M., Davila Delgado, J. M., & Akanbi, L. (2020). Deep learning models for health and safety risk prediction in power infrastructure projects. Risk Analysis, 40(10), 2019-2039. https://doi.org/10.1111/risa.13425

Inappropriate management of Health and safety (H&S) risk in power infrastructure projects can result in occupational accidents and equipment damage. Accidents at work have detrimental effects on workers, company, and the general public. Despite the a... Read More about Deep learning models for health and safety risk prediction in power infrastructure projects.

Big data analytics system for costing power transmission projects (2019)
Journal Article
Delgado, J. M. D., Oyedele, L., Bilal, M., Ajayi, A., Akanbi, L., & Akinade, O. (2020). Big data analytics system for costing power transmission projects. Journal of Construction Engineering and Management, 146(1), https://doi.org/10.1061/%28ASCE%29CO.1943-7862.0001745

© 2019 American Society of Civil Engineers. 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 wi... Read More about Big data analytics system for costing power transmission projects.

Critical success factors for ensuring bankable completion risk in PFI/PPP megaprojects (2019)
Journal Article
Owolabi, H. A., Oyedele, L. O., Alaka, H. A., Ajayi, S. O., Akinade, O. O., & Bilal, M. (2020). Critical success factors for ensuring bankable completion risk in PFI/PPP megaprojects. Journal of Management in Engineering, 36(1), https://doi.org/10.1061/%28ASCE%29ME.1943-5479.0000717

© 2019 American Society of Civil Engineers. This study investigates project financiers' perspectives on the bankability of completion risk in private finance initiative and public-private partnership (PFI/PPP) megaprojects. Using a mixed methodology... Read More about Critical success factors for ensuring bankable completion risk in PFI/PPP megaprojects.

Investigating profitability performance of construction projects using big data: A project analytics approach (2019)
Journal Article
Bilal, M., Oyedele, L. O., Kusimo, H. O., Owolabi, H. A., Akanbi, L. A., Ajayi, A. O., …Davila Delgado, J. M. (2019). Investigating profitability performance of construction projects using big data: A project analytics approach. Journal of Building Engineering, 26, Article 100850. https://doi.org/10.1016/j.jobe.2019.100850

© 2019 The Authors The construction industry generates different types of data from the project inception stage to project delivery. This data comes in various forms and formats which surpass the data management, integration and analysis capabilities... Read More about Investigating profitability performance of construction projects using big data: A project analytics approach.

Stimulating the attractiveness of PFI/PPPs using public sector guarantees (2019)
Journal Article
Owolabi, H., Oyedele, L., Alaka, H., Bilal, M., Ajayi, S., Akinade, O., & Agboola, A. (2019). Stimulating the attractiveness of PFI/PPPs using public sector guarantees. World Journal of Entrepreneurship, Management and Sustainable Development, 15(3), 239-258. https://doi.org/10.1108/WJEMSD-05-2018-0055

Purpose: Although the UK Guarantee Scheme for Infrastructures (UKGSI) was introduced in 2012 to address the huge financing gap for critical infrastructures, PFI sponsors have so far guaranteed only few projects. Many stakeholders in the project finan... Read More about Stimulating the attractiveness of PFI/PPPs using public sector guarantees.

Development of an IoT-based big data platform for day-ahead prediction of building heating and cooling demands (2019)
Journal Article
Luo, X. J., Oyedele, L. O., Ajayi, A. O., Monyei, C. G., Akinade, O. O., & Akanbi, L. A. (2019). Development of an IoT-based big data platform for day-ahead prediction of building heating and cooling demands. Advanced Engineering Informatics, 41, Article 100926. https://doi.org/10.1016/j.aei.2019.100926

© 2019 Elsevier Ltd The emerging technologies of the Internet of Things (IoT) and big data can be utilised to derive knowledge and support applications for energy-efficient buildings. Effective prediction of heating and cooling demands is fundamental... Read More about Development of an IoT-based big data platform for day-ahead prediction of building heating and cooling demands.

All Outputs (52)