The research on modeling and application of dynamic grey forecasting model based on energy price-energy consumption-economic growth

Li, Hui; Wu, Zixuan; Yuan, Xing; Yang, Yintang; He, Xiaoqiang; Duan, Huiming

doi:10.1016/j.energy.2022.124801

Cited by 15 publications

(3 citation statements)

References 32 publications

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“…The gray correlation method was used to explore the relationship between economic development and environmental protection industrial consumption, and the contribution rate was calculated based on indicators [116]. One article studied the relationship between energy, environment, and economic growth among industries and seeking high-speed economic growth with minimal environmental costs [117]. The research of industry 3E mainly focuses on optimizing energy structure, improving energy utilization efficiency, and innovating energy technology.…”

Section: Main Research Purpose Of Industrial 3ementioning

confidence: 99%

Towards Sustainable Industry: A Comprehensive Review of Energy–Economy–Environment System Analysis and Future Trends

Han,

Ren,

Liu

et al. 2024

Sustainability

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Industry, encompassing various sectors like agriculture, manufacturing, and services, is crucial for economic growth and societal progress. However, industrial growth often comes at the cost of environmental degradation and energy resource depletion, ignoring the need for sustainable development. This study analyzed 131 articles published from 2004 to 2023, using the energy–economy–environment (3E) method to explore industrial research trends. The primary focus of industrial 3E research is on environmental impact and sustainable development, particularly related to energy consumption. This field covers various topics like systems, efficiency, optimization, and others. LCA methods and system dynamics models are commonly used in research method innovation. This study summarizes the main viewpoints of industrial 3E research and highlights future research directions and priorities, including transitions to areas like agriculture, fisheries, and renewable energy and combining evaluation and prediction methods with 3E methods, conducting large-scale system research, and examining coupling relationships within and between industrial systems.

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Section: Main Research Purpose Of Industrial 3ementioning

confidence: 99%

Towards Sustainable Industry: A Comprehensive Review of Energy–Economy–Environment System Analysis and Future Trends

Han,

Ren,

Liu

et al. 2024

Sustainability

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“…With the advancement of information technology, the accessibility of information along the coal industry chain has made the short-to medium-term forecasting of coal prices feasible. Introducing forecasts of spot coal market prices can aid enterprises in making scientifically informed production management decisions based on coal market price fluctuations, thereby reducing production costs [13]. In [14], the gray difference information was used to establish a cluster of nonlinear gray prediction models of coal price based on energy price, energy consumption, and economic growth.…”

Section: Introductionmentioning

confidence: 99%

Research on Decision Optimization and the Risk Measurement of the Power Generation Side Based on Quantile Data-Driven IGDT

Liao,

Wang,

Tao

et al. 2024

Energies

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In an environment marked by dual carbon goals and substantial fluctuations in coal market prices, coal power generation enterprises face an urgent imperative to make scientifically informed decisions regarding production management amidst significant market uncertainties. To tackle this challenge, this paper proposes a methodology for optimizing electricity generation side market decisions and assessing risks using quantile data-driven information-gap decision theory (QDD-IGDT). Initially, a dual-layer decision optimization model for electricity production is formulated, taking into account coal procurement and blending processes. This model optimizes the selection of spot coal and long-term contract coal prices and simplifies the dual-layer structure into an equivalent single-layer model using the McCormick envelope and Karush–Kuhn–Tucker (KKT) conditions. Subsequently, a quantile dataset is generated utilizing a short-term coal price interval prediction model based on the quantile regression neural network (QRNN). Interval constraints on expected costs are introduced to develop an uncertainty decision risk measurement model grounded in QDD-IGDT, quantifying decision risks arising from coal market uncertainties to bolster decision robustness. Lastly, case simulations are executed by using real production data from a power generation enterprise, and the dual-layer decision optimization model is solved by employing the McCormick–KKT–Gurobi approach. Additionally, decision risks associated with coal market uncertainties are assessed through a one-dimensional search under interval constraints on expected cost volatility. The findings demonstrate the effectiveness of the proposed research methodology in cost optimization within the context of coal market uncertainties, underscoring its validity and economic efficiency.

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“…Applying the panel data model to the study of the relationship between energy consumption and economic growth has become a hot topic in the field of the domestic energy economy in China. H. Li et al studied the relationship between China's energy consumption and economic growth based on the Granger causality test model, and found that economic growth and energy consumption have a strong dependence, and the two show the characteristics of a nonlinear relationship [6]. Tian, X., et al studied the relationship between economic growth and energy consumption of 10 major countries in the world based on the fixed effect model.…”

Section: Introductionmentioning

confidence: 99%

Relationship Between Energy Consumption and Industrial Output-Based on GMM Model of Dynamic Panel Data in China

Baharum

Jin

2022

ARASET

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At present, China is in the historical intersection period of the "two centenary" goals. The realization of the "carbon neutral" goal has brought new opportunities for the development of China's energy sector. Based on the GMM estimation method of the dynamic panel model, this paper studies the relationship between energy consumption and industrial output by using China's provincial data from the years 2008 to 2018. The results show that the impact of coal consumption on industrial output tends to be declining during the sample period. However, compared with other energy consumption, it still dominates a larger proportion. Oil consumption has a negative impact on industrial output with the arrival of a production bottleneck period and the improvement of technology level. As a secondary energy, electricity consumption has an important impact on the industrial output, which is the main energy variable and is significantly positive. Other energy consumption variables have significant positive effects on industrial output. In this paper, we obtain countermeasures and suggestions on energy, such as giving full play to the decisive role of the market, improving the innovation environment in the field of energy, speed up the transformation of new and old kinetic energy.

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The research on modeling and application of dynamic grey forecasting model based on energy price-energy consumption-economic growth

Cited by 15 publications

References 32 publications

Towards Sustainable Industry: A Comprehensive Review of Energy–Economy–Environment System Analysis and Future Trends

Towards Sustainable Industry: A Comprehensive Review of Energy–Economy–Environment System Analysis and Future Trends

Research on Decision Optimization and the Risk Measurement of the Power Generation Side Based on Quantile Data-Driven IGDT

Relationship Between Energy Consumption and Industrial Output-Based on GMM Model of Dynamic Panel Data in China

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