Clemens Felsmann

Experimental Analysis of Model-Based Predictive Optimal Control for Active and Passive Building Thermal Storage Inventory

This paper demonstrates model-based predictive optimal control of active and passive building thermal storage inventory in a test facility in real time using time-of-use differentiated electricity prices without demand charges. A novel supervisory controller successfully executed a three-step procedure consisting of (1) short-term weather prediction, (2) optimization of control strategy over the next planning horizon using a calibrated building model, and (3) post-processing of the optimal strategy to yield a control command for the current time step that can be executed in the test facility. All primary and secondary building mechanical systems were effectively orchestrated by the model-based predictive optimal controller in real time while observing comfort and operational constraints. It was determined that even when the optimal controller is given imperfect weather forecasts and when the building model used for planning control strategies does not match the actual building perfectly, measured utility cost savings relative to conventional building operation can be substantial. Central requirements are a facility that lends itself to passive storage utilization and a building model that includes a realistic plant representation. Savings associated with passive building thermal storage inventory proved to be small in this case because the test facility is not an ideal candidate for the investigated control technology. Moreover, the facilitys central plant revealed the idiosyncratic behavior that the chiller operation in the ice-making mode was more energy efficient than in the chilled-water mode. Field experimentation is now required in a suitable commercial building with sufficient thermal mass, an active TES system, and a climate conducive to passive storage utilization over a longer testing period to support the laboratory findings presented in this study.

Impact of Forecasting Accuracy on Predictive Optimal Control of Active and Passive Building Thermal Storage Inventory

This paper evaluates the benefits of combined optimal control of both passive building thermal capacitance and active thermal energy storage systems to minimize total utility cost in the presence of forecasting uncertainty in the required short-term weather forecasts. Selected short-term weather forecasting models are introduced and investigated with respect to their forecasting accuracy as measured by root mean square error, mean bias error, and the coefficient of variation. The most complex model, a seasonal autoregressive integrated moving average (SARIMA), shows the worst performance, followed by a static predictor model that references standard weather archives. The best prediction accuracy is found for bin models that develop a characteristic daily profile from observations collected over the past 30 or 60 days. The model that projects yesterdays patterns one day into the future proved to be a surprisingly poor predictor. We test the predictor models in the context of a predictive optimal control task that optimizes building global temperature setpoints and active thermal energy storage charge/discharge rates in a closed-loop mode. For the four locations investigated in this article—Chicago, IL, Denver, CO, Omaha, NE, and Phoenix, AZ—it was determined that the 30-day and 60-day bin predictor models lead to utility cost savings that are only marginally inferior compared to a hypothetical perfect predictor that perfectly anticipates the weather during the next planning horizon. In summary, the predictive optimal control of active and passive building thermal storage inventory using time-of-use electrical utility rates with significant on-peak to off-peak rate differentials and demand charges is a highly promising control strategy when perfect weather forecasts are available. The primary finding of this paper is that it takes only very simple short-term prediction models to realize almost all of the theoretical potential of this technology.

An investigation of optimal control of passive building thermal storage with real time pricing

The cost savings potential of optimal passive thermal storage control were examined for day-ahead real time electricity rate structures. The operational strategies of three office building models were optimized in four US cities (Chicago, New York, Houston and Los Angeles) using price and weather data for the summer of 2008. Optimization of building thermal mass was conducted using a predictive optimal controller to define supervisory strategies in terms of building global cooling temperature setpoints. A global minimization algorithm determined optimal setpoint trajectories for each day divided into four distinct time periods. Cost savings were found to range from 0 to 14% depending on the building, climate, and characteristics of the rate signal. The best cost savings occurred for price spikes or cool nighttime temperatures. Moreover, it was found that low internal gains favoured a more flexible precooling strategy, while high internal gains coupled with low thermal mass resulted in poor precooling performance.

Advances in Near-Optimal Control of Passive Building Thermal Storage

Using a simulation and optimization environment, this paper presents advances toward near-optimal building thermal mass control derived from full factorial analyses of the important parameters influencing the passive thermal storage process for a range of buildings and climate/utility rate structure combinations. Guidelines for the application of, and expected savings from, building thermal mass control strategies that can be easily implemented and result in a significant reduction in building operating costs and peak electrical demand are sought. In response to the actual utility rates imposed in the investigated cities, fundamental insights and control simplifications are derived from those buildings deemed suitable candidates. The near-optimal strategies are derived from the optimal control trajectory, consisting of four variables, and then tested for effectiveness and validated with respect to uncertainty regarding building parameters and climate variations. Due to the overriding impact of the utility rate structure on both savings and control strategy, combined with the overwhelming diversity of utility rates offered to commercial building customers, this study cannot offer universally valid control guidelines. Nevertheless, a significant number of cases, i.e., combinations of buildings, weather, and utility rate structure, have been investigated, which offer both insights and recommendations for simplified control strategies. These guidelines represent a good starting point for experimentation with building thermal mass control for a substantial range of building types, equipments, climates, and utility rates.

Sensitivity Analysis of Optimal Building Thermal Mass Control

In order to avoid high utility demand charges from cooling during the summer and to level a buildings electrical demand profile, precooling of the buildings massive structure can be applied to shift cooling-related thermal loads in response to utility pricing signals. Several previous simulation and experimental studies have shown that proper precooling can attain considerable reduction of operating cost in buildings. This paper systematically evaluates the merits of the passive building thermal capacitance to minimize energy cost for a design day using optimal control. The evaluation is conducted by means of a sensitivity analysis utilizing a dynamic building energy simulation program coupled to a popular technical computing environment. The optimal controller predicts the required extent of precooling (zone temperature set-point depression), depending on the utility rate structure, occupancy and on-peak period duration and onset, internal gains, building mass, occupancy period temperature set-point range, and weather as characterized by diurnal temperature and relative humidity swings. In addition to quantifying the building response, energy consumption, and utility cost, this paper extracts the dominant features of the optimal precooling strategies for each of the investigated cases so that guidelines for near-optimal building thermal mass savings may be developed in the future.

An empirical validation of modelling solar gains through a glazing unit using building energy simulation programs

Empirical validation of building energy simulation tools is an important component in assessing the reliability of the simulation software. An experiment performed in conjunction with the International Energy Agencys Task 34/Annex 43 was used to assess the performance of four building energy simulation codes used to model an outdoor test cell with a glazing unit. The experiment was run for a 20-day period during October 2004, and experimental cooling powers were compared with predictions from (1) EnergyPlus, (2) DOE-2.1E, (3) TRNSYS-TUD, and (4) ESP-r. Detailed code inputs for optical and thermophysical properties as well as the impact of thermal bridges were quantified through experiments and simulations; numerous statistical parameters and sensitivity analyses were implemented to facilitate a thorough comparison of predicted and experimental cooling powers. The mean percentage differences for all four codes were: 1.9% for EnergyPlus, −3.6% for DOE-2.1E, −6.2% for TRNSYS-TUD, and 3.1% for ESP-r. The implications of various modeling procedures as well as a detailed discussion of the results are provided, specifically concerning the sensitivity of the code cooling power predictions to the selection of convective heat transfer coefficients and algorithms.

Nutzung von Solarenergie im Campusquartier

Die TU Dresden verzeichnet durch Campuserweiterungen und intensivierte Nutzung seit Jahren einen kontinuierlich ansteigenden Elektroenergieverbrauch. Im Jahr 2015 belief sich dieser auf 56 GWh [1], was nach aktuellen Angaben des BDEW [2] naherungsweise dem Stromverbrauch privater Haushalte einer Stadt mit ca. 40.000 Einwohnern entspricht. Der damit verbundene Kohlendioxid(CO2)-Ausstos erreichte mit ca. 25.000 t einen neuen Hochststand.

Propagation modelling based on airborne particle release data from nanostructured materials for exposure estimation and prediction

The gap between release and exposure is limiting the current risk assessment of nanostructured materials. Both, release and exposure were connected to each other by transport and transformation processes and require therefore the description/specification of complex exposure scenarios. Within this study, propagation modelling based on experimentally determined airborne particle release data was used for exposure estimation and prediction in a defined model room. Therefore, 9 different exposure scenarios based on 3 release scenarios and 3 ventilation scenarios were analysed. Results for near field considerations have shown that the level of inhalation exposure is fundamentally defined by the present exposure scenario, that personal heat can cause particle availability in the breathing zone and that highest exposure levels arise immediate during material processing.

Fully parallel, OpenGL-based computation of obstructed area-to-area view factors

We present a fully parallelized, high-performance, OpenGL-based approach to compute obstructed area-to-area (A2A) view factors for radiative heat transfer. The A2A view factors are computed from the defining surface integral by Gaussian quadrature. The values of the integrand, i.e. the point-to-area view factors, are computed using an OpenGL-based hemicube (HC) method to efficiently solve the obstruction problem by exploiting the hardware-accelerated visibility testing on modern graphics cards. The final steps to maximize hardware usage are rendering the HC and performing the numerical quadrature in parallel such that data transfer times are completely shadowed by computations. To demonstrate the power of our approach we compute the A2A view factor matrices for a warehouse equipped with ceramic infrared heaters and a test cabin conforming to EN 442 containing a section of a panel radiator. To judge the quality of the results we measure the deviation from unity of the area-weighted column sums of the view factor matrix and the error in radiant flux balance. Compared to a previous, ray-tracing-based implementation, we gain three orders of magnitude in speed in the view factor computation. Conservation of radiant flux is also substantially improved.