Locally stationary Energy Time Series (LETS)

很难想象日常生活的任何方面不以某种方式依赖于能源的供应和使用。每一种能源的背后都有一个复杂的利益相关者网络，确保从发电到分配和使用的可靠供应。近年来，人们越来越关注低碳能源和可再生能源，并在该行业中增加市场化，重组和私有化，特别是大型公用事业。时间序列分析是统计基石，对许多能源相关挑战至关重要。例如，短期风速预测是公用事业公司汇集多种供应来源的关键，预测客户群体未来的能源使用也是如此。时间序列分析对于规划拟议的风电场也至关重要，以确定预测的风力发电是否可能是有效和可靠的。在过去十年中，利益攸关方遇到的时间序列的性质发生了变化。在过去，序列被假设为是平稳的（即它们的统计特性不随时间而变化）。现在所经历的很多事情都是非静止的。随着越来越多的高质量数据流使人们能够提出、研究和考虑新的模型，这一点变得越来越清晰。风是间歇性的，无法控制。相比之下，天然气发电站是高度可控的，可以产生几乎恒定的功率。将大量的风力发电接入电网可能是有问题的，因为可能存在持续的无风时段或高度可变的风时段。另一个问题是日益市场化：在整个欧洲，人们现在能够从各种供应商和供应模式购买电力，分销商向市场的不同、分散的部分供应电力。因此，收集到的关于消费者或发电机的数据比往年更不稳定，也更不平稳。我们的提案正面解决了这个非平稳性的新世界。几年来，我们的团队一直处于非平稳时间序列开发的最前沿：引入新的类别，并以创新的方式使用它们。我们的建议将开发新的技术，彻底改变这种时间序列的分析方式，因此对我们的工业合作伙伴和能源行业有很大的用处。举例来说，我们会研究及发展新方法，以处理（i）同时处理超过一个非平稳数列;（ii）为数列确定适当的抽样率，以及是否有数列因抽样率不适当而受损;（iii）处理数据遗漏及时间数列间隔不规则的常见问题，但仍能获得有意义的见解;（iv）改进预测方法，并使一个时间序列能够从另一个时间序列中预测出来;（v）改进对我们估计的不确定性的可靠测量。即使在这些量化领域中的任何一个方面进行微小的改进，也可以为我们的合作伙伴和社会带来巨大的财务、环境和可靠性效益。我们打算通过转向非静止世界，逐步改变能源利益相关者使用的方法和程序。

It is difficult to think of any aspect of everyday life which does not rely in some way on energy supply and use. Behind every energy source is a complex network of stakeholders ensuring a reliable supply from generation through to distribution and use. In recent years, there has been an increasing focus on low carbon energy & renewables and also increasing marketisation, reorganisation and privatisation in the sector, particularly with large utilities.Time series analysis is a statistical cornerstone, of vital importance to many energy related challenges. For example, short-term wind speed forecasting is key for utilities aggregating many sources of supply, as is predicting the future energy use of groups of customers. Time series analysis is also critical to the planning of proposed wind farms to see if the predicted wind power is likely to be efficient and reliable. Over the last decade, the nature of time series encountered by stakeholders has changed. In the past, series were assumed to be stationary (i.e. that their statistical properties did not change over time). Much of what is now experienced is non-stationary. This becomes ever clearer as increasing flows of high-quality data enable new models to be proposed, studied and considered.Compare, for example, wind and gas-fired power. Wind is intermittent and not controllable. Gas powered stations, by comparison, are highly controllable and can produce almost constant power. Incorporating large quantities of wind power into the grid can be problematic as there can be sustained periods without wind, or periods of highly variable wind. Another issue is increasing marketisation: across Europe people are now able to purchase power from a variety of suppliers and modes of supply, distributors supply to different, fragmented parts of the market. Consequently, data collected on consumers or generators is less stable and much less stationary than in previous years.Our proposal addresses this new world of non-stationarity head-on. For several years our team has been at the forefront of developments in non-stationary time series: introducing new classes and using them in new and innovative ways. Our proposal will develop novel techniques to revolutionize the way that such time series are analyzed and hence be of considerable use to our industrial partners and the energy industry more widely. For example, we shall investigate and develop new methods for (i) handling more than one non-stationary series simultaneously; (ii) identifying appropriate sampling rates for series and whether any series have been compromised by inappropriate sampling rates; (iii) dealing with the common problem of data dropouts and irregularly spaced time series but still obtain meaningful insights; (iv) improved methods for forecasting and enabling predictions of one time series from another; (v) improving robust measures of uncertainty of our estimates. Even small improvements in any of these quantitative areas can lead to massive financial, environmental and reliability benefits of value to our partners and society more generally. We intend to create a step-change in the methods and procedures used by energy stakeholders by moving to the non-stationary world.