A short note on quantifying and visualizing yearly variation in online monitored temperature data

The paper demonstrates how seasonal variation in sequentially arriving temperature data can be visualized by the specification of landmarks and subsequent time warping. We exemplify the idea with water temperature data from the river Wupper in northwestern Germany and with air temperature data from Berlin, Germany. Landmarks are thereby based on temperature thresholds. The method allows to assess whether the seasonal variation is running ahead or behind the average

Energienachfrage-Vorhersagen und Dynamisches Wassertemperatur-Management

Mestekemper T. Energy demand forecasting and dynamic water temperature management. Bielefeld (Germany): Bielefeld University; 2011

Functional Hourly Forecasting of Water Temperature

Mestekemper T, Windmann M, Kauermann G. Functional Hourly Forecasting of Water Temperature. International Journal of Forecasting. 2010;26(4):684-699

Functional hourly forecasting of water temperature

The paper describes the problem of forecasting water temperatures on an hourly basis using previous water and air temperatures as predictors. Both time series are decomposed using functional principal components, leading to low dimensional vector autoregressive modeling. The principal component scores mirror serial correlation, which is also incorporated in the model. The modeling exercise is motivated by and demonstrated with data collected in the German river Wupper, and the approach is contrasted to alternative routines which have been suggested in statistics and hydrology.

A comparison of periodic autoregressive and dynamic factor models in intraday energy demand forecasting

Mestekemper T, Kauermann G, Smith MS. A comparison of periodic autoregressive and dynamic factor models in intraday energy demand forecasting. International Journal of Forecasting. 2013;29(1):1-12.We suggest a new approach for forecasting energy demand at an intraday resolution. The demand in each intraday period is modeled using semiparametric regression smoothing to account for calendar and weather components. Residual serial dependence is captured by one of two multivariate stationary time series models, with a dimension equal to the number of intraday periods. These are a periodic autoregression and a dynamic factor model. We show the benefits of our approach in the forecasting of (a) district heating demand in a steam network in Germany and (b) aggregate electricity demand in the state of Victoria, Australia. In both studies, accounting for weather can improve the forecast quality substantially, as does the use of time series models. We compare the effectiveness of the periodic autoregression with three variations of the dynamic factor model, and find that the dynamic factor model consistently provides more accurate forecasts. Overall, our approach combines many of the features which have previously been shown to provide high quality forecasts of energy demand over horizons of up to one week, as well as introducing some novel ones. (C) 2012 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved