Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities

Journal of Cleaner Production, Journal Year: 2021, Volume and Issue: 289, P. 125834 - 125834

Published: Jan. 9, 2021

Language: Английский

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Forecasting: theory and practice

International Journal of Forecasting, Journal Year: 2022, Volume and Issue: 38(3), P. 705 - 871

Published: Jan. 20, 2022

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds future is both exciting challenging, with individuals organisations seeking to minimise risks maximise utilities. large number forecasting applications calls for a diverse set methods tackle real-life challenges. This article provides non-systematic review theory practice forecasting. We provide an overview wide range theoretical, state-of-the-art models, methods, principles, approaches prepare, produce, organise, evaluate forecasts. then demonstrate how such theoretical concepts are applied in variety contexts. do not claim this exhaustive list applications. However, we wish our encyclopedic presentation will offer point reference rich work undertaken over last decades, some key insights practice. Given its nature, intended mode reading non-linear. cross-references allow readers navigate through various topics. complement covered by lists free or open-source software implementations publicly-available databases.

Language: Английский

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Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature

Energies, Journal Year: 2020, Volume and Issue: 13(6), P. 1473 - 1473

Published: March 20, 2020

Artificial intelligence (AI) is one of the most disruptive technologies our time. Interest in use AI for urban innovation continues to grow. Particularly, rise smart cities—urban locations that are enabled by community, technology, and policy deliver productivity, innovation, livability, wellbeing, sustainability, accessibility, good governance, planning—has increased demand AI-enabled innovations. There is, nevertheless, no scholarly work provides a comprehensive review on topic. This paper generates insights into how can contribute development smarter cities. A systematic literature selected as methodologic approach. Results categorized under main city dimensions, i.e., economy, society, environment, governance. The findings containing 93 articles disclose that: (a) context cities an emerging field research practice. (b) central focus technologies, algorithms, their current prospective applications. (c) applications mainly concentrate business efficiency, data analytics, education, energy, environmental health, land use, security, transport, management areas. (d) limited investigating risks wider utilization. (e) Upcoming disruptions societies have not been adequately examined. Current potential contributions outlined this inform scholars areas further research.

Language: Английский

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A review of the-state-of-the-art in data-driven approaches for building energy prediction

Energy and Buildings, Journal Year: 2020, Volume and Issue: 221, P. 110022 - 110022

Published: April 30, 2020

Language: Английский

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A deep learning framework for building energy consumption forecast

Renewable and Sustainable Energy Reviews, Journal Year: 2020, Volume and Issue: 137, P. 110591 - 110591

Published: Dec. 15, 2020

Language: Английский

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Short-term natural gas consumption prediction based on Volterra adaptive filter and improved whale optimization algorithm

Engineering Applications of Artificial Intelligence, Journal Year: 2019, Volume and Issue: 87, P. 103323 - 103323

Published: Nov. 11, 2019

Language: Английский

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Artificial intelligence powered large-scale renewable integrations in multi-energy systems for carbon neutrality transition: Challenges and future perspectives

Energy and AI, Journal Year: 2022, Volume and Issue: 10, P. 100195 - 100195

Published: Aug. 5, 2022

The vigorous expansion of renewable energy as a substitute for fossil is the predominant route action to achieve worldwide carbon neutrality. However, clean supplies in multi-energy building districts are still at preliminary stages paradigm transitions. In particular, technologies and methodologies large-scale integrations not sufficiently sophisticated, terms intelligent control management. Artificial (AI) techniques powered systems can learn from bio-inspired lessons provide power with intelligence. there few in-depth dissections deliberations on roles AI decarbonisation systems. This study summarizes commonly used AI-related approaches discusses their functional advantages when being applied various sectors, well contribution optimizing operational modalities improving overall effectiveness. also presents practical applications integration systems, analyzes effectiveness through theoretical explanations diverse case studies. addition, this introduces limitations challenges associated neutrality transition using relevant techniques, proposes further promising research perspectives recommendations. comprehensive review ignites advanced provides valuable informational instructions guidelines different stakeholders (e.g., engineers, designers scientists) transition.

Language: Английский

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Artificial intelligence techniques for enabling Big Data services in distribution networks: A review

Renewable and Sustainable Energy Reviews, Journal Year: 2021, Volume and Issue: 150, P. 111459 - 111459

Published: July 16, 2021

Artificial intelligence techniques lead to data-driven energy services in distribution power systems by extracting value from the data generated deployed metering and sensing devices. This paper performs a holistic analysis of artificial applications networks, ranging operation, monitoring maintenance planning. The potential for system needed sources are identified classified. following networks analyzed: topology estimation, observability, fraud detection, predictive maintenance, non-technical losses forecasting, management systems, aggregated flexibility trading. A review methods implemented each these is conducted. Their interdependencies mapped, proving that multiple can be offered as single clustered service different stakeholders. Furthermore, dependencies between AI with identified. In recent years there has been significant rise deep learning time series prediction tasks. Another finding unsupervised mainly being applied customer segmentation, buildings efficiency clustering consumption profile grouping detection. Reinforcement widely design, although more testing real environments needed. Distribution network sensorization should enhanced increased order obtain larger amounts valuable data, enabling better outcomes. Finally, future opportunities challenges applying grids discussed.

Language: Английский

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Towards collective energy Community: Potential roles of microgrid and blockchain to go beyond P2P energy trading

Applied Energy, Journal Year: 2022, Volume and Issue: 314, P. 119003 - 119003

Published: March 30, 2022

Decarbonisation of energy sector is crucial to deliver the future net zero system with promoting and facilitating large-scale electrification end-user sectors. It necessary provide sustainable, cost-effective, resilient scalable solutions exploit power citizens contribute clean transition, increasing flexibility overall system. Energy community, as new actor, create an integrated pan market by bringing together local consumers players. However, diversity community brings huge challenges in integration decentralized renewables regulated framework, interaction marketplaces, well interoperability cross-border sectors privacy, security incentives. This paper intends in-depth investigation on role microgrid blockchain, alone together, “enabling framework” boost potential transportation, building, industrial sectors, rural/remote areas islands towards a networking green ecosystem. serves comprehensive reference understand modern its control communication technology blockchain services techno-socio-economic innovations for restructuring sustainable supply chain.

Language: Английский

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Highly accurate energy consumption forecasting model based on parallel LSTM neural networks

Advanced Engineering Informatics, Journal Year: 2021, Volume and Issue: 51, P. 101442 - 101442

Published: Nov. 8, 2021

Language: Английский

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