Locally stationary Energy Time Series (LETS)

很难想象日常生活的任何方面都不以某种方式依赖于能源的供应和使用。每一种能源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，低碳能源和可再生能源受到越来越多的关注，该行业的市场化、重组和私有化程度也在不断提高，特别是在公用事业规模较大的行业。时间序列分析是统计基础，对许多与能源相关的挑战至关重要。例如，短期风速预测对于聚集许多供应来源的公用事业公司来说是关键，预测客户群体的未来能源使用也是如此。时间序列分析对于规划拟建的风力发电场也很关键，以确定预测的风力发电量是否可能有效和可靠。在过去的十年中，利益相关者遇到的时间序列的性质发生了变化。在过去，序列被假定为平稳的(即，它们的统计属性不随时间改变)。现在所经历的很多事情都是非平稳的。随着越来越多的高质量数据流动使新的模型得以提出、研究和考虑，这一点变得越来越清楚。例如，与风力和燃气发电进行比较。风是间歇性的，不能控制。相比之下，天然气发电站的可控性很强，可以产生几乎不变的电力。将大量风电并入电网可能会有问题，因为可能会有持续的无风期，或者风速高度可变的时期。另一个问题是日益市场化：在整个欧洲，人们现在能够从不同的供应商和供应模式购买电力，分销商向市场的不同、分散的部分供电。因此，与前几年相比，收集的关于消费者或发电机的数据不那么稳定，也不那么稳定。我们的建议正面解决了这个非平稳的新世界。几年来，我们的团队一直处于非平稳时间序列开发的前沿：引入新的类并以新的和创新的方式使用它们。我们的建议将开发新的技术来革命性地分析这种时间序列的方式，从而对我们的工业合作伙伴和更广泛的能源行业有相当大的用处。例如，我们将研究和开发新的方法，用于(I)同时处理多个非平稳序列；(Ii)确定序列的适当采样率，以及是否有任何序列受到不适当采样率的影响；(Iii)处理常见的数据丢失和不规则间隔的时间序列问题，但仍获得有意义的见解；(Iv)改进预测方法，以便能够对一个时间序列和另一个时间序列进行预测；(V)改进我们估计的稳健不确定性度量。在这些量化领域中的任何一个领域，即使是微小的改进，也可以为我们的合作伙伴和整个社会带来巨大的财务、环境和可靠性方面的价值收益。我们打算通过转向非固定世界来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.