A critical review of comparative global historical energy consumption and future demand: The story told so far

Energy Reports, Journal Year: 2020, Volume and Issue: 6, P. 1973 - 1991

Published: Aug. 1, 2020

This review presents a critical combined energy analysis of demand in developed/developing countries, including the load requirements various business sectors. It summarizes on-demand time-series, supply, overall trade gas, oil, electricity, coal, and renewable (e.g., wind, solar, geothermal, tidal, etc.) as well global carbon dioxide (CO2) emissions. The duration is selected between supply forecast from 1990 to 2040. Multi-energy approaches include primary generation, consumption, gross domestic product (GDP) intensity, total balance crude oil production, production natural use lignite for generation share renewables power percentage solar energy. Geographic coverage covered Organization Economic Co-operation Development (OECD), group seven (G7), Brazil, Russia, India, China, South Africa (BRICS), European Union, Europe, North America, Commonwealth Independent States (CIS), Asia, Latin Pacific, Middle-East Africa. Market individuals cooperative policymakers communicate variety ways: our its impact on trade, social development, economic climate change, which then presented deeper way, future outlook. findings make it clear that there great deal until 2040 different situations: new aspects policymaking, requirement about 15% lower 450-scenario, 10% higher current policy scenario.

Language: Английский

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A review of wind speed and wind power forecasting with deep neural networks

Applied Energy, Journal Year: 2021, Volume and Issue: 304, P. 117766 - 117766

Published: Sept. 10, 2021

Language: Английский

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Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm

Renewable and Sustainable Energy Reviews, Journal Year: 2022, Volume and Issue: 160, P. 112128 - 112128

Published: March 5, 2022

Language: Английский

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Deep learning for load forecasting with smart meter data: Online Adaptive Recurrent Neural Network

Applied Energy, Journal Year: 2020, Volume and Issue: 282, P. 116177 - 116177

Published: Nov. 10, 2020

Language: Английский

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Energetics Systems and artificial intelligence: Applications of industry 4.0

Energy Reports, Journal Year: 2021, Volume and Issue: 8, P. 334 - 361

Published: Dec. 16, 2021

Industrial development with the growth, strengthening, stability, technical advancement, reliability, selection, and dynamic response of power system is essential. Governments companies invest billions dollars in technologies to convert, harvest, rising demand, changing demand supply patterns, efficiency, lack analytics required for optimal energy planning, store energy. In this scenario, artificial intelligence (AI) starting play a major role market. Recognizing importance AI, study was conducted on seven different energetics systems their variety applications, including: i) electricity production; ii) delivery; iii) electric distribution networks; iv) storage; v) saving, new materials, devices; vi) efficiency nanotechnology; vii) policy, economics. The main drivers are four key techniques used current AI technologies, fuzzy logic systems; neural genetic algorithms; expert systems. developed countries, industry has started using connect smart meters, grids, Internet Things devices. These will lead improvement management, transparency, usage renewable energies. recent decades/years, technology brought significant improvements how devices monitor data, communicate system, analyze input–output, display data unprecedented ways. New applications become feasible when these developments incorporated into industry. But contrary, much more investment needed global research data-driven models. terms supply, can help utilities provide customers affordable from complex sources secure manner, while at same time providing opportunity use own efficiently. Moreover, policy recommendations, opportunities, 4.0 improve sustainability have been briefly described.

Language: Английский

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Using the internet of things in smart energy systems and networks

Sustainable Cities and Society, Journal Year: 2021, Volume and Issue: 68, P. 102783 - 102783

Published: Feb. 21, 2021

Language: Английский

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Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach

Energy, Journal Year: 2021, Volume and Issue: 240, P. 122812 - 122812

Published: Dec. 4, 2021

An accurate solar energy forecast is of utmost importance to allow a higher level integration renewable into the controls existing electricity grid. With availability data in unprecedented granularities, there an opportunity use data-driven algorithms for improved prediction generation. In this paper, generally applicable stacked ensemble algorithm (DSE-XGB) proposed utilizing two deep learning namely artificial neural network (ANN) and long short-term memory (LSTM) as base models forecast. The predictions from are integrated using extreme gradient boosting enhance accuracy PV generation model was evaluated on four different datasets provide comprehensive assessment. Additionally, shapely additive explanation framework utilized study deeper insight mechanism algorithm. performance by comparing results with individual ANN, LSTM, Bagging. DSE-XGB method exhibits best combination consistency stability case studies irrespective weather variations demonstrates improvement R2 value 10%–12% other models.

Language: Английский

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A review and taxonomy of wind and solar energy forecasting methods based on deep learning

Energy and AI, Journal Year: 2021, Volume and Issue: 4, P. 100060 - 100060

Published: March 7, 2021

Renewable energy is essential for planet sustainability. output forecasting has a significant impact on making decisions related to operating and managing power systems. Accurate prediction of renewable vital ensure grid reliability permanency reduce the risk cost market Deep learning's recent success in many applications attracted researchers this field its promising potential manifested richness proposed methods increasing number publications. To facilitate further research development area, paper provides review deep learning-based solar wind published during last five years discussing extensively data datasets used reviewed works, pre-processing methods, deterministic probabilistic evaluation comparison methods. The core characteristics all works are summarised tabular forms enable methodological comparisons. current challenges future directions given. trends show that hybrid models most followed by Recurrent Neural Network including Long Short-Term Memory Gated Unit, third place Convolutional Networks. We also find multistep ahead gaining more attention. Moreover, we devise broad taxonomy using key insights gained from extensive review, believe will be understanding cutting-edge accelerating innovation field.

Language: Английский

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Physical energy and data-driven models in building energy prediction: A review

Energy Reports, Journal Year: 2022, Volume and Issue: 8, P. 2656 - 2671

Published: Feb. 10, 2022

The difficulty in balancing energy supply and demand is increasing due to the growth of diversified flexible building resources, particularly rapid development intermittent renewable being added into power grid. accuracy consumption prediction top priority for electricity market management ensure grid safety reduce financial risks. speed load are fundamental prerequisites different objectives such as long-term planning short-term optimization systems buildings past few decades have seen impressive time series forecasting models focusing on domains objectives. This paper presents an in-depth review discussion models. Three widely used approaches, namely, physical (i.e., white box), data-driven black hybrid grey were classified introduced. principles, advantages, limitations, practical applications each model investigated. Based this review, research priorities future directions domain highlighted. conclusions drawn could guide prediction, therefore facilitate efficiency buildings.

Language: Английский

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Interpretable machine learning for building energy management: A state-of-the-art review

Advances in Applied Energy, Journal Year: 2023, Volume and Issue: 9, P. 100123 - 100123

Published: Jan. 13, 2023

Machine learning has been widely adopted for improving building energy efficiency and flexibility in the past decade owing to ever-increasing availability of massive operational data. However, it is challenging end-users understand trust machine models because their black-box nature. To this end, interpretability attracted increasing attention recent studies helps users decisions made by these models. This article reviews previous that interpretable techniques management analyze how model improved. First, are categorized according application stages techniques: ante-hoc post-hoc approaches. Then, analyzed detail specific with critical comparisons. Through review, we find broad faces following significant challenges: (1) different terminologies used describe which could cause confusion, (2) performance ML tasks difficult compare, (3) current prevalent such as SHAP LIME can only provide limited interpretability. Finally, discuss future R&D needs be accelerate management.

Language: Английский

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