Localised high resolution forecasting for energy demand based on smart meter data

基于智能电表数据的本地化高分辨率能源需求预测

• 批准号: 2423198
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2423198
• 关键词: Localised; resolution; forecasting; energy; demand

This is a PhD in Energy and Artificial Intelligence. Using smart meter data provided by the partner utility company, this PhD will investigate the applicability of bottom-up forecasting models for energy demand trained on historical data from individual smart meters.This PhD will investigate, using smart meter data provided by the partner utility, how bottom-up forecasting models can be used to predict energy demand at a number of scales. These models will be trained on historical data from individual smart meters, and the forecasts will be aggregated at a local/regional level and compared with the accuracy of top down forecasts to assess what benefits exist and what level they occur.Particular avenues of exploration could be:- Spatial information allowing exploration of scenarios such as the impact of Locational Marginal Pricing (nodal pricing), a policy implemented in the United States and much discussed in the UK, on a utility based on its current portfolio of customers and the impact of the spatial distribution of its customers on profitability.- the potential for balancing portfolios of customers, locally or within the suppliers base, and identifying locations of possible network constraints and substations likely to reach capacity with the uptake of electric vehicles.- Design of an optimal trading agent for the hedging and purchasing of electricity given the portfolio of smart meter information is a likely final application of the research that would bring together a number of the individual strands of the project.Methods:As a starting point, state-of-the-art ensembling methods will be tested as a benchmark before exploration of the potential for deep learning methods in particular Long-Short-Term-Memory Neural Networks to improve results. Reinforcement learning and agent-based models will also be explored throughout the course of the project.

这是一个能源和人工智能的博士学位。使用合作伙伴公用事业公司提供的智能电表数据，本博士将调查基于个人智能电表历史数据训练的自下而上能源需求预测模型的适用性。本博士将使用合作伙伴公用事业公司提供的智能电表数据，调查如何使用自下而上预测模型在多个尺度上预测能源需求。这些模型将根据来自单个智能电表的历史数据进行训练，预测将在本地/区域级别进行聚合，并与自上而下预测的准确性进行比较，以评估存在哪些好处以及它们发生在什么级别。具体的探索途径可以是：-空间信息，允许探索诸如位置边际定价(节点定价)的影响等场景，这是一种在美国实施并在英国经常讨论的政策，基于其当前的客户组合及其客户的空间分布对盈利能力的影响。-平衡客户组合的潜力，本地或供应商基础内的客户组合，以及确定可能的网络限制和变电站可能随着电动汽车的普及而达到能力的位置。-在给定智能电表信息组合的情况下，设计用于对冲和购买电力的最佳交易代理可能是这项研究的最终应用，该研究将把该项目的一些单独的线条聚集在一起。方法：作为起点，在探索深度学习方法，特别是长期-短期记忆神经网络的潜力之前，将测试最先进的集合方法作为基准，以改进结果。强化学习和基于代理的模型也将在整个项目过程中进行探索。