Dual stream network with attention mechanism for photovoltaic power forecasting

Applied Energy, Journal Year: 2023, Volume and Issue: 338, P. 120916 - 120916

Published: March 20, 2023

Language: Английский

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A review on enhancing energy efficiency and adaptability through system integration for smart buildings

Journal of Building Engineering, Journal Year: 2024, Volume and Issue: 89, P. 109354 - 109354

Published: April 18, 2024

Language: Английский

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Smart home energy management systems: Research challenges and survey

Alexandria Engineering Journal, Journal Year: 2024, Volume and Issue: 92, P. 117 - 170

Published: March 5, 2024

Electricity is establishing ground as a means of energy, and its proportion will continue to rise in the next generations. Home energy usage expected increase by more than 40% 20 years. Therefore, compensate for demand requirements, proper planning strategies are needed improve home management systems (HEMs). One crucial aspects HEMS load forecasting scheduling utilization. Energy depend heavily on precise scheduling. Considering this scenario, article was divided into two parts. Firstly, gives thorough analysis models HEMs with primary goal determining whichever model most appropriate given situation. Moreover, optimal utilization HEMs, current literature has discussed number optimization approaches. secondly article, these approaches be examined thoroughly develop effective operating make wise judgments regarding techniques HEMs. Finally, paper also presents future technical advancements research gaps how they affect activities near future.

Language: Английский

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Optimizing NZEB Performance: A Review of Design Strategies and Case Studies

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 103950 - 103950

Published: Jan. 1, 2025

Language: Английский

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Building energy consumption prediction and optimization using different neural network-assisted models; comparison of different networks and optimization algorithms

Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 127, P. 107356 - 107356

Published: Nov. 9, 2023

Language: Английский

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A deep learning framework using multi-feature fusion recurrent neural networks for energy consumption forecasting

Applied Energy, Journal Year: 2023, Volume and Issue: 348, P. 121563 - 121563

Published: July 15, 2023

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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The Influence of Artificial Intelligence on E-Governance and Cybersecurity in Smart Cities: A Stakeholder’s Perspective

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 69783 - 69797

Published: Jan. 1, 2023

Artificial intelligence (AI) has been identified as a critical technology of Fourth Industrial Revolution (Industry 4.0) for protecting computer network systems against cyber-attacks, malware, phishing, damage, or illicit access. AI potential in strengthening the cyber capabilities and safety nation-states, local governments, non-state entities through e-Governance. Existing research provides mixed association between AI, e-Governance, cybersecurity; however, this relationship is believed to be context-specific. cybersecurity influence are affected by various stakeholders possessing variety knowledge expertise respective areas. To fill context specific gap, study investigates direct cybersecurity. Furthermore, examines mediating role e-Governance moderating effect involvement on The results PLS-SEM path modeling analysis revealed partial impact Likewise, was discovered well It implies that vital significance because all have interest vibrant, transparent, secured cyberspace while using e-services. This practical implications governmental bodies smart cities their measures.

Language: Английский

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A comprehensive review of artificial intelligence and machine learning applications in energy consumption and production

Journal of Technology Innovations and Energy, Journal Year: 2023, Volume and Issue: 2(4), P. 1 - 26

Published: Oct. 19, 2023

The energy industry worldwide is today confronted with several challenges, including heightened levels of consumption and inefficiency, volatile patterns in demand supply, a dearth crucial data necessary for effective management. Developing countries face significant challenges due to the widespread occurrence unauthorized connections electricity grid, resulting substantial amounts unmeasured unpaid consumption. Nevertheless, implementation artificial intelligence (AI) machine learning (ML) technologies has potential improve management, efficiency, sustainability. Therefore, this study aims evaluate influence AI ML on progress industry. present employed systematic literature review methodology examine arising from frequent power outages limited accessibility various developing nations. results indicate that possess domains, predictive maintenance turbines, optimization consumption, management grids, prediction prices, assessment efficiency residential buildings. This concluded discussion measures enable nations harness advantages sector.

Language: Английский

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A Systematic Review on the Use of AI for Energy Efficiency and Indoor Environmental Quality in Buildings

Sustainability, Journal Year: 2024, Volume and Issue: 16(9), P. 3627 - 3627

Published: April 26, 2024

Global warming, climate change and the energy crisis are trending topics around world, especially within sector. The rising cost of energy, greenhouse gas (GHG) emissions global temperatures stem from over-reliance on fossil fuel as major resource. These challenges have highlighted need for alternative resources urgent intervention strategies like consumption reduction improving efficiency. heating, ventilation, air-conditioning (HVAC) system in a building accounts about 70% consumption, decision to reduce may impact indoor environmental quality (IEQ) building. It is important adequately balance tradeoff between IEQ management. Artificial intelligence (AI)-based solutions being explored performance without compromising IEQ. This paper systematically reviews recent studies AI machine learning (ML) management by exploring common use areas, methods or algorithms applied results obtained. overall purpose this research add existing body work highlight energy-related applications buildings related gaps. result shows five application areas: thermal comfort air (IAQ) control; prediction; temperature anomaly detection; HVAC controls. Gaps involving policy, real-life scenario applications, insufficient study visual acoustic areas also identified. Very few take into consideration follow standards selection process positioning sensors buildings. reveals more summarized research.

Language: Английский

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