Green building practices to integrate renewable energy in the construction sector: a review

Environmental Chemistry Letters, Journal Year: 2023, Volume and Issue: 22(2), P. 751 - 784

Published: Dec. 15, 2023

Abstract The building sector is significantly contributing to climate change, pollution, and energy crises, thus requiring a rapid shift more sustainable construction practices. Here, we review the emerging practices of integrating renewable energies in sector, with focus on types, policies, innovations, perspectives. sources include solar, wind, geothermal, biomass fuels. Case studies Seattle, USA, Manama, Bahrain, are presented. Perspectives comprise self-sufficiency, microgrids, carbon neutrality, intelligent buildings, cost reduction, storage, policy support, market recognition. Incorporating wind into buildings can fulfill about 15% building's requirements, while solar integration elevate contribution 83%. Financial incentives, such as 30% subsidy for adoption technologies, augment appeal these innovations.

Language: Английский

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A review on microgrid optimization with meta-heuristic techniques: Scopes, trends and recommendation

Energy Strategy Reviews, Journal Year: 2024, Volume and Issue: 51, P. 101298 - 101298

Published: Jan. 1, 2024

Microgrids (MGs) use renewable sources to meet the growing demand for energy with increasing consumer needs and technological advancement. They operate independently as small-scale networks using distributed resources. However, intermittent nature of poor power quality are essential operational problems that must be mitigated improve MG's performance. To address these challenges, researchers have introduced heuristic optimization mechanisms MGs. local minima inability find a global minimum in methods create errors non-linear nonconvex optimization, posing challenges dealing several aspects MG such management cost-effective dispatch, dependability, storage sizing, cyber-attack minimization, grid integration. These affect performance by adding complexity capacity, cost reliability assurance, balance sources, which accelerates need meta-heuristic algorithms (MHOAs). This paper presents state-of-the-art review MHOAs their role improving Firstly, fundamentals discussed explore scopes, requisites, opportunities networks. Secondly, domain described, recent trends techno-economic analysis, load forecasting, resiliency improvement, control operation, fault diagnosis, summarized. The summary reveals nearly 25% research areas utilizes particle swarm method, while genetic grey wolf utilized 10% 5% works studied this paper, respectively, optimizing result summarizes MHOA system-agnostic approach, offering new avenue enhancing effectiveness future Finally, we highlight some emerge during integration into MGs, potentially motivating conduct further studies area.

Language: Английский

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Machine learning scopes on microgrid predictive maintenance: Potential frameworks, challenges, and prospects

Renewable and Sustainable Energy Reviews, Journal Year: 2023, Volume and Issue: 190, P. 114088 - 114088

Published: Nov. 16, 2023

Language: Английский

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Digital technologies for a net-zero energy future: A comprehensive review

Renewable and Sustainable Energy Reviews, Journal Year: 2024, Volume and Issue: 202, P. 114681 - 114681

Published: July 2, 2024

Language: Английский

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A Stackelberg game-based peer-to-peer energy trading market with energy management and pricing mechanism: A case study in Guangzhou

Solar Energy, Journal Year: 2024, Volume and Issue: 270, P. 112388 - 112388

Published: Feb. 14, 2024

Language: Английский

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Review of deep learning techniques for power generation prediction of industrial solar photovoltaic plants

Solar Compass, Journal Year: 2023, Volume and Issue: 8, P. 100061 - 100061

Published: Oct. 30, 2023

Varying power generation by industrial solar photovoltaic plants impacts the steadiness of electric grid which necessitates prediction accurately. In this study, a comprehensive updated review standalone and hybrid machine learning techniques for PV forecasting is presented. Forecasting importance sustainability also to achieve UN sustainable development targets 2030. The comparison shows that grouping datasets based on input feature similarity, results in higher accuracy. Long-Short Term Memory (LSTM) found perform better than other deep networks all time horizons. Gate Recurrent Unit (GRU), with few trainings, be small LSTM. Based more complicated data patterns, novel architecture Deep Learning Network model, capability analyze forecast presented considering factors influencing generation. study researchers, industry, electricity distribution companies worldwide.

Language: Английский

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Techno-economic feasibility analysis of a commercial grid-connected photovoltaic plant with battery energy storage-achieving a net zero energy system

Journal of Energy Storage, Journal Year: 2023, Volume and Issue: 77, P. 109984 - 109984

Published: Dec. 14, 2023

Language: Английский

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Short-Term Solar Irradiance Forecasting Using Deep Learning Techniques: A Comprehensive Case Study

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 119851 - 119861

Published: Jan. 1, 2023

Reliable estimation of solar irradiance is required for many energy applications such as photovoltaics, water heating, cooking, microgrids, etc. Deep Learning techniques have shown outstanding behaviour analysing complex datasets efficiently with high accuracy. Multi-Layer Perceptron, Long-Short Term Memory, and Gated Recurrent Unit are found to be the most competitive in literature forecasting. Therefore, this study, a comparative analysis those models carried out using eleven years NASA satellite data training testing. The grid search technique used optimize networks architectures ensure best performance forecasting daily global irradiance. results show that all almost same accuracy mean square error close 0.017 kWh/m2/day. However, speed complexity differ each model. MLP efficient model due low number parameters. study importance reliable any location worldwide.

Language: Английский

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Machine Learning Models for Solar Power Generation Forecasting in Microgrid Application Implications for Smart Cities

Sustainability, Journal Year: 2024, Volume and Issue: 16(14), P. 6087 - 6087

Published: July 17, 2024

In the context of escalating concerns about environmental sustainability in smart cities, solar power and other renewable energy sources have emerged as pivotal players global effort to curtail greenhouse gas emissions combat climate change. The precise prediction generation holds a critical role seamless integration effective management systems within microgrids. This research delves into comparative analysis two machine learning models, specifically Light Gradient Boosting Machine (LGBM) K Nearest Neighbors (KNN), with objective forecasting microgrid applications. study meticulously evaluates these models’ accuracy, reliability, training times, memory usage, providing detailed experimental insights optimizing utilization driving forward. comparison between LGBM KNN models reveals significant performance differences. model demonstrates superior accuracy an R-squared 0.84 compared KNN’s 0.77, along lower Root Mean Squared Error (RMSE: 5.77 vs. 6.93) Absolute (MAE: 3.93 4.34). However, requires longer times (120 s 90 s) higher usage (500 MB 300 MB). Despite computational differences, exhibits stability across diverse time frames seasons, showing robustness handling outliers. These findings underscore its suitability for applications, offering enhanced strategies crucial advancing sustainability. provides essential sustainable practices lays foundation cleaner future, emphasizing importance accurate planning operation.

Language: Английский

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Leveraging Artificial Intelligence for Enhanced Sustainable Energy Management

Journal of Sustainability for Energy, Journal Year: 2024, Volume and Issue: 3(1), P. 1 - 20

Published: Feb. 4, 2024

The integration of Artificial Intelligence (AI) into sustainable energy management presents a transformative opportunity to elevate the sustainability, reliability, and efficiency systems. This article conducts an exhaustive analysis critical aspects concerning AI-sustainable nexus, encompassing challenges in technological facilitation intelligent decision-making processes pivotal for frameworks. It is demonstrated that AI applications, ranging from optimization algorithms predictive analytics, possess revolutionary capacity bolster energy. However, this not without its challenges, which span complexities socio-economic impacts. underscores imperative deploying manner transparent, equitable, inclusive. Best practices solutions are proposed navigate these effectively. Additionally, discourse extends recent advancements AI, including edge computing, quantum explainable offering insights evolving landscape Future research directions delineated, emphasizing importance enhancing explainability, mitigating bias, advancing privacy-preserving techniques, examining ramifications, exploring models human-AI collaboration, fortifying security measures, evaluating impact emerging technologies. comprehensive aims inform academics, practitioners, policymakers, guiding creation resilient future.

Language: Английский

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