Analysis of artificial intelligence-based technologies and approaches on sustainable entrepreneurship

Technological Forecasting and Social Change, Год журнала: 2022, Номер 186, С. 122152 - 122152

Опубликована: Ноя. 11, 2022

Язык: Английский

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Applications of artificial intelligence‐based modeling for bioenergy systems: A review

GCB Bioenergy, Год журнала: 2021, Номер 13(5), С. 774 - 802

Опубликована: Фев. 18, 2021

Abstract Bioenergy is widely considered a sustainable alternative to fossil fuels. However, large‐scale applications of biomass‐based energy products are limited due challenges related feedstock variability, conversion economics, and supply chain reliability. Artificial intelligence (AI), an emerging concept, has been applied bioenergy systems in recent decades address those challenges. This paper reviewed 164 articles published between 2005 2019 that different AI techniques systems. review focuses on identifying the unique capabilities various addressing bioenergy‐related research improving performance Specifically, we characterized studies by their input variables, output techniques, dataset size, performance. We examined throughout life cycle identified four areas which mostly applied, including (1) prediction biomass properties, (2) process conversion, pathways technologies, (3) biofuel properties end‐use systems, (4) modeling optimization. Based review, particularly useful generating data hard be measured directly, traditional models end‐uses, overcoming computing for design For future research, efforts needed develop standardized practical procedures selecting determining training samples, enhance collection, documentation, sharing across areas, explore potential supporting development from holistic perspectives.

Язык: Английский

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Artificial intelligence for sustainable energy: A contextual topic modeling and content analysis

Sustainable Computing Informatics and Systems, Год журнала: 2022, Номер 35, С. 100699 - 100699

Опубликована: Фев. 11, 2022

Язык: Английский

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Deep learning for renewable energy forecasting: A taxonomy, and systematic literature review

Journal of Cleaner Production, Год журнала: 2022, Номер 384, С. 135414 - 135414

Опубликована: Дек. 15, 2022

Язык: Английский

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Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead

Energies, Год журнала: 2023, Номер 16(10), С. 4060 - 4060

Опубликована: Май 12, 2023

Short-term load forecasting (STLF) is critical for the energy industry. Accurate predictions of future electricity demand are necessary to ensure power systems’ reliable and efficient operation. Various STLF models have been proposed in recent years, each with strengths weaknesses. This paper comprehensively reviews some models, including time series, artificial neural networks (ANNs), regression-based, hybrid models. It first introduces fundamental concepts challenges STLF, then discusses model class’s main features assumptions. The compares terms their accuracy, robustness, computational efficiency, scalability, adaptability identifies approach’s advantages limitations. Although this study suggests that ANNs may be most promising ways achieve accurate additional research required handle multiple input features, manage massive data sets, adjust shifting conditions.

Язык: Английский

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Multi-scale solar radiation and photovoltaic power forecasting with machine learning algorithms in urban environment: A state-of-the-art review

Journal of Cleaner Production, Год журнала: 2023, Номер 426, С. 139040 - 139040

Опубликована: Сен. 30, 2023

Язык: Английский

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Improved informer PV power short-term prediction model based on weather typing and AHA-VMD-MPE

Energy, Год журнала: 2024, Номер 307, С. 132766 - 132766

Опубликована: Авг. 10, 2024

Язык: Английский

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Predicting photovoltaic power production using high-uncertainty weather forecasts

Applied Energy, Год журнала: 2023, Номер 339, С. 120989 - 120989

Опубликована: Март 28, 2023

Язык: Английский

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Short-term solar radiation forecasting using hybrid deep residual learning and gated LSTM recurrent network with differential covariance matrix adaptation evolution strategy

Energy, Год журнала: 2023, Номер 278, С. 127701 - 127701

Опубликована: Май 10, 2023

Язык: Английский

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Advances in solar forecasting: Computer vision with deep learning

Advances in Applied Energy, Год журнала: 2023, Номер 11, С. 100150 - 100150

Опубликована: Авг. 7, 2023

Renewable energy forecasting is crucial for integrating variable sources into the grid. It allows power systems to address intermittency of supply at different spatiotemporal scales. To anticipate future impact cloud displacements on generated by solar facilities, conventional modeling methods rely numerical weather prediction or physical models, which have difficulties in assimilating information and learning systematic biases. Augmenting computer vision with machine overcomes some these limitations fusing real-time cover observations surface measurements acquired from multiple sources. This Review summarizes recent progress multisensor Earth a focus deep learning, provides necessary theoretical framework develop architectures capable extracting relevant data ground-level sky cameras, satellites, stations, sensor networks. Overall, has potential significantly improve accuracy robustness meteorology; however, more research realize this its limitations.

Язык: Английский

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