Dynamic Deep Learning for Electricity Demand Forecasting

该项目旨在开发一种深度学习技术，用于高分辨率电力需求预测和住宅需求响应建模。电力消费数据是动态的，具有很大的不确定性。深度学习技术期望提供准确的需求预测，从而能够最佳地利用现有的 电网资产和指导未来投资。预期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整合到电网中，从而提供可靠的能源，支持澳大利亚配电网络规划和运营中的数据驱动决策。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。

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.