Energy cost forecasting and financial strategy optimization in smart grids via ensemble algorithm

IF 2.6 4区 工程技术 Q3 ENERGY & FUELS Frontiers in Energy Research Pub Date : 2024-08-29 DOI:10.3389/fenrg.2024.1353312
Juanjuan Yang
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Abstract

IntroductionIn the context of energy resource scarcity and environmental pressures, accurately forecasting energy consumption and optimizing financial strategies in smart grids are crucial. The high dimensionality and dynamic nature of the data present significant challenges, hindering accurate prediction and strategy optimization.MethodsThis paper proposes a fusion algorithm for smart grid enterprise decision-making and economic benefit analysis, aiming to enhance decision-making accuracy and predictive capability. The method combines deep reinforcement learning (DRL), long short-term memory (LSTM) networks, and the Transformer algorithm. LSTM is utilized to process and analyze time series data, capturing historical patterns of energy prices and usage. Subsequently, DRL and the Transformer algorithm are employed to further analyze the data, enabling the formulation and optimization of energy purchasing and usage strategies.ResultsExperimental results demonstrate that the proposed approach outperforms traditional methods in improving energy cost prediction accuracy and optimizing financial strategies. Notably, on the EIA Dataset, the proposed algorithm achieves a reduction of over 48.5% in FLOP, a decrease in inference time by over 49.8%, and an improvement of 38.6% in MAPE.DiscussionThis research provides a new perspective and tool for energy management in smart grids. It offers valuable insights for handling other high-dimensional and dynamically changing data processing and decision optimization problems. The significant improvements in prediction accuracy and strategy optimization highlight the potential for widespread application in the energy sector and beyond.
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通过集合算法优化智能电网中的能源成本预测和财务策略
引言 在能源资源稀缺和环境压力的背景下,准确预测智能电网的能源消耗和优化财务策略至关重要。本文提出了一种用于智能电网企业决策和经济效益分析的融合算法,旨在提高决策的准确性和预测能力。该方法结合了深度强化学习(DRL)、长短期记忆(LSTM)网络和变压器算法。LSTM 用于处理和分析时间序列数据,捕捉能源价格和使用的历史模式。实验结果表明,所提出的方法在提高能源成本预测准确性和优化财务策略方面优于传统方法。值得注意的是,在 EIA 数据集上,所提算法的 FLOP 降低了 48.5%,推理时间减少了 49.8%,MAPE 提高了 38.6%。这项研究为智能电网中的能源管理提供了新的视角和工具,并为处理其他高维和动态变化的数据处理和决策优化问题提供了有价值的见解。预测准确性和策略优化方面的重大改进凸显了在能源领域及其他领域广泛应用的潜力。
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来源期刊
Frontiers in Energy Research
Frontiers in Energy Research Economics, Econometrics and Finance-Economics and Econometrics
CiteScore
3.90
自引率
11.80%
发文量
1727
审稿时长
12 weeks
期刊介绍: Frontiers in Energy Research makes use of the unique Frontiers platform for open-access publishing and research networking for scientists, which provides an equal opportunity to seek, share and create knowledge. The mission of Frontiers is to place publishing back in the hands of working scientists and to promote an interactive, fair, and efficient review process. Articles are peer-reviewed according to the Frontiers review guidelines, which evaluate manuscripts on objective editorial criteria
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