Dynamic Prediction and Driving Factors of Carbon Emission in Beijing, China, under Carbon Neutrality Targets

Atmosphere, Journal Year: 2023, Volume and Issue: 14(5), P. 798 - 798

Published: April 27, 2023

China has made remarkable achievements in reducing carbon emissions recent years. However, there is still much reduction room before achieving neutrality. In Beijing, the capital of China, it a strategic choice to respond global climate change by promoting green and low-carbon development. This paper calculates dioxide key industries Beijing analyzes temporal evolution trend emissions. Carbon 2030 are predicted based on grey prediction GM (1,1) BP neural network model. The effects factors discussed using threshold regression model under different economic conditions. results show that energy consumption intensity, GDP per capita, ownership civil cars have positive impact emissions, while number permanent residents urban space areas negative These findings emission influencing contribute path design. Related policy implications put forward from aspects industrial upgrading, accelerating construction advanced structures, optimizing transportation strengthening building

Language: Английский

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A data decomposition and attention mechanism-based hybrid approach for electricity load forecasting

Complex & Intelligent Systems, Journal Year: 2024, Volume and Issue: 10(3), P. 4103 - 4118

Published: March 2, 2024

Abstract An accurate and reliable prediction of future energy patterns is utmost significance for the smooth operation several related activities such as capacity or generation unit planning, transmission network optimization, better resources availability, many more. With availability historical load datasets through smart grid systems, artificial intelligence machine learning-based techniques have been extensively developed achieving desired objectives. However, effectively capturing strong randomness non-linear fluctuations in time-series remains a critical issue that demands concrete solutions. Considering this, current research proposes hybrid approach amalgamating data smoothing decomposition strategy with deep neural models improving forecasting results. Moreover, an attention mechanism integrated to capture relevant portions time series, thus ability long-term dependencies among demand observations. This integration enhances generalization capabilities proposed model. To validate performance benefits achieved by approach, comparative evaluation conducted state-of-the-art neural-based series models. The assessment carried out on novel real-world dataset five southern states India, superiority variations well observed demonstrated terms indicators.

Language: Английский

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Time series forecasting via integrating a filtering method: an application to electricity consumption

Computational Statistics, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 13, 2025

Language: Английский

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A grey prediction model based on Von Bertalanffy equation and its application in energy prediction

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 143, P. 110012 - 110012

Published: Jan. 15, 2025

Language: Английский

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Forecasts and Analysis of Economic Outputs for Chinese High-Tech Industries: Insights from Spatial–Temporal Information Fusion

Computational Economics, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 14, 2025

Language: Английский

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Hierarchical Time Series Forecasting of COVID-19 Cases Using County-Level Clustering Data

Operations Research Forum, Journal Year: 2025, Volume and Issue: 6(1)

Published: Feb. 20, 2025

Language: Английский

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Half-hourly electricity price prediction model with explainable-decomposition hybrid deep learning approach

Energy and AI, Journal Year: 2025, Volume and Issue: unknown, P. 100492 - 100492

Published: March 1, 2025

Language: Английский

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Emission forecasting from open burning of crop straw and policy analysis: The case for China

Energy Reports, Journal Year: 2023, Volume and Issue: 9, P. 5659 - 5669

Published: May 15, 2023

The open burning of crop straw releases greenhouse and harmful gases, pollutants, which hinder the reduction carbon emissions attainment environmental protection commitments in China. In this study, based on fractional discrete grey model (FDGM (1,1)) new information priority (NIPDGM (1,1)), an alternative weighted hybrid (WHDGM coupled with a particle swarm optimization algorithm was developed to forecast total production, quantity burning, results have shown that proposed WHDGM (1,1) had highest simulation accuracy compared NIPDGM FDGM (1,1). Based (1,1), predictions for annual induced CO, CO2, NOx, PM2.5 are conducted, respectively. By 2025, production will increase by 10.5% 7.2%, Relevant be augmented 7.4%, 7.7%, 5.6%, 9.6%, Countermeasures controlling relevant policy suggestions been discussed. This study offers practical insights guidance strategic control therefore, ensuring achievement neutrality supporting commitment.

Language: Английский

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The general conformable fractional grey system model and its applications

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 136, P. 108817 - 108817

Published: June 25, 2024

Language: Английский

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Spatial-temporal evolution characteristics and driving factors analysis of regional energy supply and demand in China

Energy Strategy Reviews, Journal Year: 2024, Volume and Issue: 55, P. 101542 - 101542

Published: Sept. 1, 2024

Language: Английский

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