Dynamic Deep Learning for Electricity Demand Forecasting

用于电力需求预测的动态深度学习

• 批准号: LP180101309
• 负责人: Prof Mahdi Jalili
• 金额: $ 22.74万
• 依托单位: RMIT University
• 依托单位国家: 澳大利亚
• 项目类别: Linkage Projects
• 财政年份: 2020
• 资助国家: 澳大利亚
• 起止时间: 2020-04-13 至 2022-12-03
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/LP180101309
• 关键词: Dynamic; Deep; Learning; Electricity; Demand

This project aims at developing a deep learning technology for high resolution electricity demand forecasting and residential demand response modelling. Electricity consumption data are dynamic and highly uncertain. The deep learning technology expects to provide accurate demand forecasting, and thus enabling optimal use of existing grid assets and guiding future investments. The expected outcome can support data-driven decision-making in Australia's electricity distribution network planning and operation by considering future challenges such as integrating battery storage and electric vehicles into the grid, and thus providing reliable energy. The project expects to train next generation expert workforce for Australia's future power grid.

该项目旨在为高分辨率的电力需求预测和住宅需求响应建模开发深度学习技术。电力消耗数据是动态的，高度不确定。深度学习技术期望提供准确的需求预测，从而实现现有的最佳使用 网格资产和指导未来的投资。预期的结果可以通过考虑未来的挑战，例如将电池存储和电动汽车集成到网格中，从而提供可靠的能源，从而支持澳大利亚的电力分销网络计划和运营中以数据为导向的决策。该项目预计将为澳大利亚未来的电网培训下一代专家劳动力。