a b s t r a c tThis paper presents an investigation of how Model Predictive Control (MPC) and weather predictions can increase the energy efficiency in Integrated Room Automation (IRA) while respecting occupant comfort. IRA deals with the simultaneous control of heating, ventilation and air conditioning (HVAC) as well as blind positioning and electric lighting of a building zone such that the room temperature as well as CO 2 and luminance levels stay within given comfort ranges. MPC is an advanced control technique which, when applied to buildings, employs a model of the building dynamics and solves an optimization problem to determine the optimal control inputs. In this paper it is reported on the development and analysis of a Stochastic Model Predictive Control (SMPC) strategy for building climate control that takes into account the uncertainty due to the use of weather predictions.As first step the potential of MPC was assessed by means of a large-scale factorial simulation study that considered different types of buildings and HVAC systems at four representative European sites. Then for selected representative cases the control performance of SMPC, the impact of the accuracy of weather predictions, as well as the tunability of SMPC were investigated. The findings suggest that SMPC outperforms current control practice.

Spatially and temporally high-resolution estimates of past natural climate variability are important to assess recent significant climate trends. The mid-latitude atmospheric circulation is the dominant factor for regional changes in temperature, rainfall, and other climatic variables. Here we present reconstructions of gridded monthly sea level pressure (SLP) fields back to 1659 and seasonal reconstructions from 1500-1658 for the eastern North Atlantic-European region (30°W to 40°E; 30°N to 70°N). These were developed using principal component regression analysis based on the combination of early instrumental station series (pressure, temperature and precipitation) and documentary proxy data from Eurasian sites. The relationships were derived over the 1901-1960 calibration period and verified over 1961-1990. Under the assumption of stationarity in the statistical relationships, a transfer function derived over the 1901-1990 period was used to reconstruct the 500-year largescale SLP fields. Systematic quality testing indicated reliable winter reconstructions throughout the entire period. Lower skill was obtained for the other seasons, although meaningful monthly reconstructions were available from around 1700 onwards, when station pressure series became available. The quality and the reconstructed SLP fields for two exceptionally cold years (1573, 1740) are discussed and climatologically interpreted. An EOF analysis of the 1500-1999 winter SLP revealed, firstly, a zonal flow pattern with pronounced decadal to centenial time scale variations, secondly, a monopole pattern over northwest Europe and thirdly, a pattern modulating the meridional flow component over Europe. These 500year SLP reconstructions should be useful for modelling studies, particulary for analyses of low-frequency atmospheric variability and for circulation dynamics.

Abstract. Instrumental station pressure, temperature and precipitation measurements and proxy data were used to statistically reconstruct monthly time series of the North Atlantic Oscillation (NAO) and the Eurasian (EU) circulation indices back to 1675. Systematic testing of the reconstruction procedure indicated generally reliable reconstructions throughout the entire period, except for summertime before about 1750. Predictive skill varied for different sub-periods depending on data availability. It was highest for autumn and winter and was generally better for the EU than for the NAO index. Wavelet analysis suggested significant low-frequency variability, especially for the spring, summer and annual averaged indices. The co-variability between the NAO and EU indices was found to exhibit large decadal to century timescale variations, indicating that climate variability over the continent is temporarily decoupled from the NAO.

Abstract-One of the most critical challenges facing society today is climate change and thus the need to realize massive energy savings. Since buildings account for about 40% of global final energy use, energy efficient building climate control can have an important contribution. In this paper we develop and analyze a Stochastic Model Predictive Control (SMPC) strategy for building climate control that takes into account weather predictions to increase energy efficiency while respecting constraints resulting from desired occupant comfort. We investigate a bilinear model under stochastic uncertainty with probabilistic, time varying constraints. We report on the assessment of this control strategy in a large-scale simulation study where the control performance with different building variants and under different weather conditions is studied. For selected cases the SMPC approach is analyzed in detail and shown to significantly outperform current control practice.