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En el presente trabajo de investigación se busca demostrar la efectividad de la herramienta de planeamiento Look Ahead, de la Programación Ultimo Planificador (Last Planner), cuando es aplicada correctamente en un proyecto inmobiliario multifamiliar de 10 pisos. La necesidad de cumplir con fechas pactadas de entrega de los productos terminados y validados a los clientes, es la razón de los proyectos inmobiliarios, por un tema de cumplimiento, imagen y como motivo principal, evitar los sobrecostos; por tal razón, se hace indispensable buscar ayuda en los diferentes sistema de gestión de proyectos, para usarlos como herramientas de planificación y programación que ayuden a tomar el control efectivo de los proyectos para así garantizar el cumplimiento de los tiempos de ejecución, en las diferentes fases que la componen.

The Ensemble Kalman Filter (EnKF) is a popular data assimilation method for state-parameter estimation. Following a sequential assimilation strategy, it breaks the problem into alternating cycles of forecast and analysis steps. In the forecast step, the dynamical model is used to integrate a stochastic sample approximating the state analysis distribution (called analysis ensemble) to obtain a forecast ensemble. In the analysis step, the forecast ensemble is updated with the incoming observation using a Kalman-like correction, which is then used for the next forecast step. In realistic large-scale applications, EnKFs are implemented with limited ensembles, and often poorly known model errors statistics, leading to a crude approximation of the forecast covariance. This strongly limits the filter performance. Recently, a new EnKF was proposed in [1] following a one-step-ahead smoothing strategy (EnKF-OSA), which involves an OSA smoothing of the state between two successive analysis. At each time step, EnKF-OSA exploits the observation twice. The incoming observation is first used to smooth the ensemble at the previous time step. The resulting smoothed ensemble is then integrated forward to compute a "pseudo forecast" ensemble, which is again updated with the same observation. The idea of constraining the state with future observations is to add more information in the estimation process in order to mitigate for the sub-optimal character of EnKF-like methods. The second EnKF-OSA "forecast" is computed from the smoothed ensemble and should therefore provide an improved background. In this work, we propose a deterministic variant of the EnKF-OSA, based on the Singular Evolutive Interpolated Ensemble Kalman (SEIK) filter. The motivation behind this is to avoid the observations perturbations of the EnKF in order to improve the scheme's behavior when assimilating big data sets with small ensembles. The new SEIK-OSA scheme is implemented and its efficiency is demonstrated by performing assimilation experiments with the highly nonlinear Lorenz model and a realistic setting of the Advanced Circulation (ADCIRC) model configured for storm surge forecasting in the Gulf of Mexico during Hurricane Ike.

This thesis compares the simple Autoregressive (AR) model against the k- Nearest Neighbor (k-NN) model to make a point forecast of five energy commodity prices. Those commodities are natural gas, heating oil, gasoline, ethanol, and crude oil. The data for the commodities are monthly and, for each commodity, two-thirds of the data are used for an in-sample forecast, and the remaining one-third of the data are used to perform an out-of-sample forecast. Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) are used to compare the two forecasts. The results showed that one method is superior by one measure but inferior by another. Although the differences of the two models are minimal, it is up to a decision maker as to which model to choose. The Diebold-Mariano (DM) test was performed to test the relative accuracy of the models. For all five commodities, the results failed to reject the null hypothesis indicating that both models are equally accurate.

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In this master thesis the possibilities of combining an adaptive cruise control with information about the road ahead has been studied. The focus has been to investigate the possibility to save fuel by using information about road topology, Look-Ahead. An adaptive cruise control, AiCC, is used when there are preceding vehicles and when the driver in addition to choosing a desired travel speed for the vehicle also chooses a desired time gap that is to be kept to preceding vehicles travelling slower than the own vehicle. Using information about the road ahead and information of preceding vehicles a controller with the function to adapt the speed to the preceding vehicle, target, and at the same time reduce the fuel usage has been constructed. The controller considers the topography on the road and the distance to the target to be able to reduce the utilization of the brakes in steep downhills and to reduce fuel by slowing down before the downhill and then gain speed due to the gravitational force. The controller uses the assumption that the target travels with constant velocity at all time. The work has included simulations with two different test roads, one in Sweden with shorter and not so steep downhills. The other road is placed in Germany and has long and steep downhills. In the simulations three various time gaps, 1, 2 and 3 seconds, has been used and three different weights of the preceding vehicle, 20, 40 and 50 tonnes. The vehicle with the controller using adaptive cruise control and Look-Ahead has a weight of 40 tonnes. The results shows that fuel can be saved, using information about the road ahead in combination with an adaptive cruise control. The best result is obtained when the road contains steep and long downills, where the vehicle will gain speed due to the gravitational force. For the long and steep downhills the result is best when the target weight is 40 and 50 tonnes. When the downhills are smaller and not so steep the best result is obtained when the target weight is 20 tonnes. For these downhills the assumption that the target travels at constant speed makes the vehicle reduce the speed too much before the downhill, not considering that a heavier vehicle will accelerate in the downhill as well. The time gaps that gives the best result is time gap 1 second. This is due to that the aerodynamic force acting upon the vehicle is reduced when there is a preceding vehicle at a not too far distance. The smaller the distance to the preceding vehicle the more the aerodynamic force is reduced.

Look ahead cruise control deals with the concept of using recorded topographic road data combined with a GPS to control vehicle speed. The purpose of this is to save fuel without a change in travel time for a given road. This thesis explores the sensitivity of different disturbances for look ahead systems. Two different systems are investigated, one using a simple precalculated speed trajectory without feedback and the second based upon a model predictive control scheme with dynamic programming as optimizing algorithm. Defect input data like bad positioning, disturbed angle data, faults in mass estimation and wrong wheel radius are discussed in this thesis. Also some investigations of errors in the environmental model for the systems are done. Simulations over real road profiles with two different types of quantization of the road slope data are done. Results from quantization of the angle data in the system are important since quantization will be unavoidable in an implementation of a topographic road map. The results from the simulations shows that disturbance of the fictive road profiles used results in quite large deviations from the optimal case. For the recorded real road sections however the differences are close to zero. Finally conclusions of how large deviations from real world data a look ahead system can tolerate are drawn.

<p>Look ahead cruise control deals with the concept of using recorded topographic road data combined with a GPS to control vehicle speed. The purpose of this is to save fuel without a change in travel time for a given road. This thesis explores the sensitivity of different disturbances for look ahead systems. Two different systems are investigated, one using a simple precalculated speed trajectory without feedback and the second based upon a model predictive control scheme with dynamic programming as optimizing algorithm.</p><p>Defect input data like bad positioning, disturbed angle data, faults in mass estimation and wrong wheel radius are discussed in this thesis. Also some investigations of errors in the environmental model for the systems are done. Simulations over real road profiles with two different types of quantization of the road slope data are done. Results from quantization of the angle data in the system are important since quantization will be unavoidable in an implementation of a topographic road map.</p><p>The results from the simulations shows that disturbance of the fictive road profiles used results in quite large deviations from the optimal case. For the recorded real road sections however the differences are close to zero. Finally conclusions of how large deviations from real world data a look ahead system can tolerate are drawn.</p>

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Increasing penetration of stochastic photovoltaic (PV) generation into the electric power system poses significant challenges to system operators. In the thesis, we evaluate the spatial and temporal correlations of stochastic PV generation at multiple sites. Given the unique spatial and temporal correlation of PV generation, an optimal data-driven forecast model for short-term PV power is proposed. This model leverages both spatial and temporal correlations among neighboring solar sites, and is shown to have improved performance compared with conventional persistent model. The tradeoff between communication cost and improved forecast quality is studied using realistic data sets collected from California and Colorado. n IEEE 14 bus system test case is used to quantify the value of improved forecast quality through the reduction of system dispatch cost. The Modified spatio-temporal forecast model which has the least forecast PV overestimate percentage shows the best performance in the dispatch cost reduction.