Heteroscedastic Gaussian processes for uncertainty modeling in large-scale crowdsourced traffic data

Accurately modeling traffic speeds is a fundamental part of efficient intelligent transportation systems. Nowadays, with the widespread deployment of GPS-enabled devices, it has become possible to crowdsource the collection of speed information to road users (e.g. through mobile applications or dedicated in-vehicle devices). Despite its rather wide spatial coverage, crowdsourced speed data also brings very important challenges, such as the highly variable measurement noise in the data due to a variety of driving behaviors and sample sizes. When not properly accounted for, this noise can severely compromise any application that relies on accurate traffic data. In this article, we propose the use of heteroscedastic Gaussian processes (HGP) to model the time-varying uncertainty in large-scale crowdsourced traffic data. Furthermore, we develop a HGP conditioned on sample size and traffic regime (SRC-HGP), which makes use of sample size information (probe vehicles per minute) as well as previous observed speeds, in order to more accurately model the uncertainty in observed speeds. Using 6 months of crowdsourced traffic data from Copenhagen, we empirically show that the proposed heteroscedastic models produce significantly better predictive distributions when compared to current state-of-the-art methods for both speed imputation and short-term forecasting tasks.Comment: 22 pages, Transportation Research Part C: Emerging Technologies (Elsevier

Flight Data of Airplane for Wind Forecasting

This research solely focuses on understanding and predicting weather behavior, which is one of the important factors that affect airplanes in flight. The future weather information is used for informing pilots about changing flight conditions. In this paper, we present a new approach towards forecasting one component of weather information, wind speed, from data captured by airplanes in flight. We compare NASA’s ACT-America project against NOAA’s Wind Aloft program for prediction suitability. A collinearity analysis between these datasets reveals better model performance and smaller test error with NASA’s dataset. We then apply machine learning and a genetic algorithm to process the data further and arrive at a competitive error rate. The sliding window approach is used to find the best window size, and then we create a forecasting model that predicts wind speed at high altitudes 10 mins ahead of time. Finally, a stacking-based framework was used for better performance than individual learning algorithms to get root means square error (RMSE) of the best combination as 0.674, which is 98.4% better than the state-of-the-art approach

Integrated building control based on occupant behavior pattern detection and local weather forecasting

ABSTRACT Standard office building control systems operate the heating, ventilating, and air conditioning on a fixed schedule, based upon anticipated occupancy and use of the building. This study introduces and illustrates a method for integrated building heating, cooling and ventilation control to reduce energy consumption and maintain indoor temperature set points, based on the prediction of occupant behaviour patterns and local weather conditions. The experiment test-bed is setup in the Solar Decathlon Hous

Operational Variables for improving industrial wind turbine Yaw Misalignment early fault detection capabilities using data-driven techniques

This is the author accepted manuscript. The final version is available from the Institute of Electrical and Electronics Engineers via the DOI in this recordOffshore wind turbines are complex pieces of engineering and are, generally, exposed to harsh environmental conditions that are making them to susceptible unexpected and potentially catastrophic damage. This results in significant down time, and high maintenance costs. Therefore, early detection of major failures is important to improve availability, boost power production and reduce maintenance costs. This paper proposes a SCADA data based Gaussian Process (GP) (a data-driven, machine learning approach) fault detection algorithm where additional model inputs, called operational variables (pitch angle and rotor speed) are used. Firstly, comparative studies of these operational variables are carried out to establish whether the parameter leads to improved early fault detection capability; it is then used to construct an improved GP fault detection algorithm. The developed model is then validated against existing methods in terms of capability to detect in advance (and by how much) signs of failure with a low false positive rate. Failure due to yaw misalignment results in significant down time and a reduction in power production was found to be a useful case study to demonstrate the effectiveness of the proposed algorithms. Historical SCADA 10-minute data obtained from pitch-regulated turbines were used for models training and validation purposes. Results show that (i) the additional model inputs were able to improve the accuracy of GP power curve models with rotor speed responsible for a significant improvement in performance; (ii) the inclusion of rotor speed enhanced early failure detection without any false positives, in contrast to the other methods investigated.U.S. Department of Commerc

Probabilistic Short-Term Wind Power Forecasting Using Sparse Bayesian Learning and NWP

Probabilistic short-term wind power forecasting is greatly significant for the operation of wind power scheduling and the reliability of power system. In this paper, an approach based on Sparse Bayesian Learning (SBL) and Numerical Weather Prediction (NWP) for probabilistic wind power forecasting in the horizon of 1–24 hours was investigated. In the modeling process, first, the wind speed data from NWP results was corrected, and then the SBL was used to build a relationship between the combined data and the power generation to produce probabilistic power forecasts. Furthermore, in each model, the application of SBL was improved by using modified-Gaussian kernel function and parameters optimization through Particle Swarm Optimization (PSO). To validate the proposed approach, two real-world datasets were used for construction and testing. For deterministic evaluation, the simulation results showed that the proposed model achieves a greater improvement in forecasting accuracy compared with other wind power forecast models. For probabilistic evaluation, the results of indicators also demonstrate that the proposed model has an outstanding performance

Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression

Renewable energy from wind and solar resources can contribute significantly to the decarbonisation of the conventionally fossil-driven electricity grid. However, their seamless integration with the grid poses significant challenges due to their intermittent generation patterns, which is intensified by the existing uncertainties and fluctuations from the demand side. A resolution is increasing energy storage and standby power generation which results in economic losses. Alternatively, enhancing the predictability of wind and solar energy as well as demand enables replacing such expensive hardware with advanced control and optimization systems. The present research contribution establishes consistent sets of data and develops data-driven models through machine-learning techniques. The aim is to quantify the uncertainties in the electricity grid and examine the predictability of their behaviour. The predictive methods that were selected included conventional artificial neural networks (ANN), support vector regression (SVR) and Gaussian process regression (GPR). For each method, a sensitivity analysis was conducted with the aim of tuning its parameters as optimally as possible. The next step was to train and validate each method with various datasets (wind, solar, demand). Finally, a predictability analysis was performed in order to ascertain how the models would respond when the prediction time horizon increases. All models were found capable of predicting wind and solar power, but only the neural networks were successful for the electricity demand. Considering the dynamics of the electricity grid, it was observed that the prediction process for renewable wind and solar power generation, and electricity demand was fast and accurate enough to effectively replace the alternative electricity storage and standby capacity

Robust planning for autonomous parafoil

Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2013.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 112-119).Parafoil trajectory planning systems must be able to accurately guide the highly non-linear, under-actuated parafoil system from the drop zone to the pre-determined impact point. Parafoil planning systems are required to navigate highly complex terrain scenarios, particularly in the presence of an uncertain and potentially highly dynamic wind environment. This thesis develops a novel planning approach to parafoil terminal guidance. Building on the chance-constrained rapidly exploring random tree (CC-RRT) [1] algorithm, this planner, CC-RRT with Analytic Sampling, considers the non-linear dynamics, as well as the under-actuated control authority of the parafoil by construction. Additionally, CC-RRT with Analytic Sampling addresses two important limitations to state-of-the-art parafoil trajectory planners: (1) implicit or explicit constraints on starting altitude of the terminal guidance phase, and (2) a reactive or limitedly-proactive approach to handling the eect of wind uncertainty. This thesis proposes a novel formulation for the cost-to-go function, utilizing an approximation of the reachability set for the parafoil to account for the eect of vehicle heading on potential future states. This cost-to-go function allows for accurate consideration of partially planned paths, effectively removing strict constraints on starting altitude of the terminal guidance phase. The reachability set cost-to-go function demonstrates considerably improved performance over a simple LQR cost function, as well as cost-to-go functions with a glide-slope cone bias, demonstrating the eectiveness of utilizing the reachability set approximation as a means for incorporating heading dynamics. Furthermore, this thesis develops a multi-class model for characterizing the uncertain effect of wind. The wind model performs an online classication based on the observed wind measurements in order to determine the appropriate level of planner conservatism. Coupling this wind model with the method for sampling the analytic uncertainty distribution presented in this thesis, the CCRRT with Analytic Sampling planner is able to eciently account for the future eect of wind uncertainty and adjust trajectory plans accordingly, allowing the planner to operate in arbitrary terrain configurations without issue. CC-RRT with Analytic Sampling performs exceptionally well in complex terrain scenarios. Simulation results demonstrate signicant improvement on complex terrain relative to the state-of-the-art Band-Limited Guidance (BLG) [2], drastically reducing the worst case and average target miss distances. Simulation results demonstrate the CC-RRT with Analytic Sampling algorithm remains un-affected as terrain complexity increases, making it an ideal choice for applications where difficult terrain is an issue, as well as missions with targets with drastically dierent terrain conditions. Moreover, CC-RRT with Analytic Sampling is capable of starting terminal guidance at significantly higher altitudes than conventional approaches, while demonstrating no signicant change in performance.by Ian Sugel.S.M

Robust planning for autonomous parafoil

Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2013.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 112-119).Parafoil trajectory planning systems must be able to accurately guide the highly non-linear, under-actuated parafoil system from the drop zone to the pre-determined impact point. Parafoil planning systems are required to navigate highly complex terrain scenarios, particularly in the presence of an uncertain and potentially highly dynamic wind environment. This thesis develops a novel planning approach to parafoil terminal guidance. Building on the chance-constrained rapidly exploring random tree (CC-RRT) [1] algorithm, this planner, CC-RRT with Analytic Sampling, considers the non-linear dynamics, as well as the under-actuated control authority of the parafoil by construction. Additionally, CC-RRT with Analytic Sampling addresses two important limitations to state-of-the-art parafoil trajectory planners: (1) implicit or explicit constraints on starting altitude of the terminal guidance phase, and (2) a reactive or limitedly-proactive approach to handling the eect of wind uncertainty. This thesis proposes a novel formulation for the cost-to-go function, utilizing an approximation of the reachability set for the parafoil to account for the eect of vehicle heading on potential future states. This cost-to-go function allows for accurate consideration of partially planned paths, effectively removing strict constraints on starting altitude of the terminal guidance phase. The reachability set cost-to-go function demonstrates considerably improved performance over a simple LQR cost function, as well as cost-to-go functions with a glide-slope cone bias, demonstrating the eectiveness of utilizing the reachability set approximation as a means for incorporating heading dynamics. Furthermore, this thesis develops a multi-class model for characterizing the uncertain effect of wind. The wind model performs an online classication based on the observed wind measurements in order to determine the appropriate level of planner conservatism. Coupling this wind model with the method for sampling the analytic uncertainty distribution presented in this thesis, the CCRRT with Analytic Sampling planner is able to eciently account for the future eect of wind uncertainty and adjust trajectory plans accordingly, allowing the planner to operate in arbitrary terrain configurations without issue. CC-RRT with Analytic Sampling performs exceptionally well in complex terrain scenarios. Simulation results demonstrate signicant improvement on complex terrain relative to the state-of-the-art Band-Limited Guidance (BLG) [2], drastically reducing the worst case and average target miss distances. Simulation results demonstrate the CC-RRT with Analytic Sampling algorithm remains un-affected as terrain complexity increases, making it an ideal choice for applications where difficult terrain is an issue, as well as missions with targets with drastically dierent terrain conditions. Moreover, CC-RRT with Analytic Sampling is capable of starting terminal guidance at significantly higher altitudes than conventional approaches, while demonstrating no signicant change in performance.by Ian Sugel.S.M

New optimal power flow techniques to improve integration of distributed generation in responsive distribution networks

Climate change has brought about legally-binding targets for Scotland, the U.K. and the E.U. to reduce greenhouse gas emissions and source a share of overall energy consumption from renewable energy resources by 2020. With severe limitations in the transport and heating sectors the onus is on the electricity sector to provide a significant reduction in greenhouse gas emissions and introduce a substantial increase in renewable energy production. The most attractive renewable energy resources are located in the geographic extremes of the country, far from the large population densities and high voltage, high capacity transmission networks. This means that the majority of renewable generation technologies will need to connect to the conventionally passive, lower voltage distribution networks. The integration of Distributed Generation (DG) is severely restricted by the technical limitations of the passively managed lower voltage infrastructure. Long lead times and the capital expenditure of traditional electricity network reinforcement can significantly delay or make the economics of some renewable generation schemes unviable. To be able to quickly and cost-effectively integrate significant levels of DG, the conventional fit-and-forget approach will have to be evolved into a ‘connect-and-manage’ system using active network management (ANM) techniques. ANM considers the real-time variation in generation and demand levels and schedules electricity network control settings to alleviate system constraints and increase connectable capacity of DG. This thesis explores the extent to which real time adjustments to DG and network asset controller set-points could allow existing networks to accommodate more DG. This thesis investigates the use of a full AC OPF technique to operate and schedule in real time variables of ANM control in distribution networks. These include; DG real and reactive power output and on-load-tap-changing transformer set-points. New formulations of the full AC OPF problem including multi-objective functions, penalising unnecessary deviation of variable control settings, and a Receding-Horizon formulation are assessed. This thesis also presents a methodology and modelling environment to explore the new and innovative formulations of OPF and to assess the interactions of various control practices in real time. Continuous time sequential, single scenario, OPF analyses at a very short control cycle can lead to the discontinuous and unnecessary switching of network control set-points, particularly during the less onerous network operating conditions. Furthermore, residual current flow and voltage variation can also gave rise to undesirable network effects including over and under voltage excursion and thermal overloading of network components. For the majority of instances, the magnitude of constraint violation was not significant but the levels of occurrence gave occasional cause for concern. The new formulations of the OPF problem were successful in deterring any extreme and unsatisfactory effects. Results have shown significant improvements in the energy yield from non-firm renewable energy resources. Initial testing of the real time OPF techniques in a simple demonstration network where voltage rise restricted the headroom for installed DG capacity and energy yield, showed that the energy yield for a single DG increased by 200% from the fit-and-forget scenario. Extrapolation of the OPF technique to a network with multiple DGs from different types of renewable energy resources showed an increase of 216% from the fit-and-forget energy yield. In a much larger network case study, where thermal loading limits constrained further DG capacity and energy yield, the increase in energy yield was more modest with an average increase of 45% over the fit-and-forget approach. In the large network where thermal overloading prevailed there was no immediate alternative to real power curtailment. This work has demonstrated that the proposed ANM OPF schemes can provide an intelligent, more cost effective and quicker alternative to network upgrades. As a result, DNOs can have a better knowledge and understanding of the capabilities and technical limitations of their networks to absorb DG safely and securely, without the expense of conventional network reinforcement