A novel algorithm for energy market risk forecasting

Wang, Xiaofei; Pei, Pei

doi:10.1016/j.compeleceng.2022.107813

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…Another important topic is forecasting energy prices in the power system [212][213][214][215]. As Fraunholz [216] presents in his work, increasing the accuracy of forecasting market prices by just 1% can bring multi-billion savings for operators.…”

Section: Aspects Related To Forecasting Energy Pricesmentioning

confidence: 99%

Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering—Introduction to the Special Issue

Pijarski,

Belowski

2024

Energies

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The challenges currently faced by network operators are difficult and complex. Presently, various types of energy sources with random generation, energy storage units operating in charging or discharging mode and consumers with different operating characteristics are connected to the power grid. The network is being expanded and modernised. This contributes to the occurrence of various types of network operating states in practice. The appearance of a significant number of objects with random generation in the power system complicates the process of planning and controlling the operation of the power system. It is therefore necessary to constantly search for new methods and algorithms that allow operators to adapt to the changing operating conditions of the power grid. There are many different types of method in the literature, with varying effectiveness, that have been or are used in practice. So far, however, no one ideal, universal method or methodology has been invented that would enable (with equal effectiveness) all problems faced by the power system to be solved. This article presents an overview and a short description of research works available in the literature in which the authors have used modern methods to solve various problems in the field of power engineering. The article is an introduction to the special issue entitled Advances in the Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering. It is an overview of various current problems and the various methods used to solve them, which are used to cope with difficult situations. The authors also pointed out potential research gaps that can be treated as areas for further research.

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Section: Aspects Related To Forecasting Energy Pricesmentioning

confidence: 99%

Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering—Introduction to the Special Issue

Pijarski,

Belowski

2024

Energies

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“…With the vigorous development of China's rural collective economy, the smooth operation and sustainable development of the energy market has become a crucial guarantee to support agricultural production and rural revitalization (Wang & Pei, 2022). This study is dedicated to comprehensively exploring the impact of the energy market on the rural economy and the future development trend through an in-depth analysis of energy market forecasting and risk assessment based on China's rural collective economy(Ren-Wei et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Energy Market Prediction and Risk Assessment Based on China's Rural Collective Economy

Liu,

Wang,

Zhao

2024

EAI Endorsed Trans Energy Web

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INTRODUCTION: Energy, as a core element supporting the functioning of modern society, is vital to the development of the rural collective economy. With the upgrading of the agrarian industrial structure and the improvement of rural electrification levels, the energy demand gradually increases. Therefore, for China's rural collective economy, an in-depth study of the forecasting and risk assessment of the energy market has essential theoretical and practical value for scientific planning of resource allocation and improving energy utilization efficiency. OBJECTIVES: This study aims to reveal the development trend and key influencing factors through an in-depth analysis of China's rural collective economy's energy market and to make scientific forecasts of the future development of the energy market. At the same time, through risk assessment, it proposes risk prevention and resolution countermeasures of the energy market for the rural collective economy to provide decision support for rural energy security and sustainable development. METHODS: This study adopts a comprehensive analysis approach, combining historical data, policy literature analysis, and expert interviews. First, a comprehensive analytical framework is established by combing the development history of the rural collective economy energy market over the past few years. Second, quantitative analysis models and numerical simulations are used to analyze the key factors affecting the energy market. Finally, expert interviews are conducted to obtain the views of experts in related fields on the future development and risks of the energy market to improve the research conclusions further. RESULTS: The results of the study show that the energy market of China's rural collective economy will show a trend of gradual growth, but it also faces multiple risk challenges, including market price fluctuations, policy adjustments, and an imbalance between supply and demand. In the future, with the deepening of green energy policies, rural collective economies will pay more attention to the application of clean and renewable energy. CONCLUSION: To summarize, this study provides a scientific reference for the energy strategy decision-making of rural collective economies by forecasting and assessing the risk of the energy market based on China's rural collaborative economies. In the future, it is necessary to pay more attention to the improvement of the policy system to promote the development of green energy, as well as the establishment of a sound market regulatory mechanism to reduce the uncertainty of the energy market and provide solid support for the sustainable development of the rural collective economy.

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Guest editorial: Introduction to the special section on Application of Advanced Machine/Deep Learning in Electrical Power and Energy Systems (VSI-mlep)

Baziar¹,

Khan

Parsa³

2022

Computers and Electrical Engineering

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A novel algorithm for energy market risk forecasting

Cited by 3 publications

References 12 publications

Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering—Introduction to the Special Issue

Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering—Introduction to the Special Issue

Energy Market Prediction and Risk Assessment Based on China's Rural Collective Economy

Guest editorial: Introduction to the special section on Application of Advanced Machine/Deep Learning in Electrical Power and Energy Systems (VSI-mlep)

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