Future System States Research Articles

Ecosystem Model Skill Assessment. Yes We Can!

Need to Assess the Skill of Ecosystem ModelsAccelerated changes to global ecosystems call for holistic and integrated analyses of past, present and future states under various pressures to adequately understand current and projected future system states. Ecosystem models can inform management of human activities in a complex and changing environment, but are these models reliable? Ensuring that models are reliable for addressing management questions requires evaluating their skill in representing real-world processes and dynamics. Skill has been evaluated for just a limited set of some biophysical models. A range of skill assessment methods have been reviewed but skill assessment of full marine ecosystem models has not yet been attempted.Northeast US Atlantis Marine Ecosystem ModelWe assessed the skill of the Northeast U.S. (NEUS) Atlantis marine ecosystem model by comparing 10-year model forecasts with observed data. Model forecast performance was compared to that obtained from a 40-year hindcast. Multiple metrics (average absolute error, root mean squared error, modeling efficiency, and Spearman rank correlation), and a suite of time-series (species biomass, fisheries landings, and ecosystem indicators) were used to adequately measure model skill. Overall, the NEUS model performed above average and thus better than expected for the key species that had been the focus of the model tuning. Model forecast skill was comparable to the hindcast skill, showing that model performance does not degenerate in a 10-year forecast mode, an important characteristic for an end-to-end ecosystem model to be useful for strategic management purposes.Skill Assessment Is Both Possible and AdvisableWe identify best-practice approaches for end-to-end ecosystem model skill assessment that would improve both operational use of other ecosystem models and future model development. We show that it is possible to not only assess the skill of a complicated marine ecosystem model, but that it is necessary do so to instill confidence in model results and encourage their use for strategic management. Our methods are applicable to any type of predictive model, and should be considered for use in fields outside ecology (e.g. economics, climate change, and risk assessment).

Intelligent Transportation Control based on Proactive Complex Event Processing

Complex Event Processing (CEP) has become the key part of Internet of Things (IoT). Proactive CEP can predict future system states and execute some actions to avoid unwanted states which brings new hope to intelligent transportation control. In this paper, we propose a proactive CEP architecture and method for intelligent transportation control. Based on basic CEP technology and predictive analytic technology, a networked distributed Markov decision processes model with predicting states is proposed as sequential decision model. A Q-learning method is proposed for this model. The experimental evaluations show that this method works well when used to control congestion in in intelligent transportation systems.

Research Digest: Toxicity Screening of Influents Using Bioluminescent Reporter Technology

Influent toxicity can be a critical problem for publicly owned treatment works that use the activated sludge process as part of their treatment regime. In this project, the researchers developed two protocols for the screening of wastewater treatment plant influent for toxicity. Both protocols are based on a genetically engineered bioluminescent bacterium designated Shk1. Scientists at the University of Tennessee’s Center for Environmental Biotechnology constructed Shk1 from a host Pseudomonas strain isolated from an industrial wastewater treatment plant (WWTP). The first of the two Shk1-based assays that were developed utilizes a batch-wise sampling technique for analyzing grab-samples from industrial effluent, WWTP influent, and the various operations in an activated sludge WWTP (aeration basin, clarifier, etc.). The second method utilizes a continuous sampling technique and is designed for continuous monitoring of the wastewater treatment plant influent upstream of the activated sludge process. The researchers used the batch Shk1 assay to test the influent, activated sludge, and clarifier supernatant in a bench-scale wastewater treatment plant subjected to shock loads of metals (zinc, copper, nickel, and cadmium) for toxicity. They compared data on the repression of bioluminescence to activated sludge respirometry and conventional measures of plant performance (effluent ammonia and chemical oxygen demand, COD). In general, they found that the Shk1 assay indicated toxicity at levels similar to that indicated by activated sludge respirometry. However, no trend could be established between increasing Shk1 toxicity response and effluent quality. Effluent COD and NH3 data (when available) showed little or no significant effect or were highly varible. The researchers used the Shk1-based continuous toxicity screening method to generate toxicity data for a large suite of metals and synthetic organic compounds. They compared these results to literature data for toxicity as indicated by activated sludge respirometry and by the P. phosphoreum-based assay. In general, the Shk1 system gave EC50 values similar to those found in the literature for activated sludge respirometry for 102 organic compounds and to concentrations found to affect activated sludge for seven metals.The researchers adapted the continuous monitoring system for field application and installed it immediately downstream of the effluent from the primary clarifier in a municipal wastewater treatment system. They compared data from the Shk1-based system to plant performance data. During the time of the field study, no significant event occurred during which the operation of the plant was seriously impaired. Therefore, the researchers compared the Shk1 signal to the operations data provided by the plant personnel to determine if any correlation existed between the signal from Shk1 and minor fluctuations in the operations data. They found no simple quantitative relationship between the signal from the toxicity monitoring system and the plant performance data. They applied principal component and factor analysis to the Shk1data and 20 additional plant variables. The results of these analyses showed that 10 principal components were needed to account for 90% of the variability of the data and that the signal from Shk1 was therefore not sufficient to predict the system state in the absence of a major toxic event without knowledge of the values of other operating variables. In summary, these analyses indicated that the Shk1 signal would be a valuable addition to models to predict the future system state from the influent, operating, and effluent variables but it is not a sufficient variable by itself. This title belongs to WERF Research Report Series ISBN: 9781843396314 (eBook)

A model‐based approach for analog spatio‐temporal dynamic forecasting

Analog forecasting has been applied in a variety of fields for predicting future states of complex nonlinear systems that require flexible forecasting methods. Past analog methods have almost exclusively been used in an empirical framework without the structure of a model‐based approach. We propose a Bayesian model framework for analog forecasting, building upon previous analog methods but accounting for parameter uncertainty. Thus, unlike traditional analog forecasting methods, the use of Bayesian modeling allows one to rigorously quantify uncertainty to obtain realistic posterior predictive distributions. The model is applied to the long‐lead time forecasting of mid‐May averaged soil moisture anomalies in Iowa over a high‐resolution grid of spatial locations. Sea surface temperature is used to find past time periods with similar trajectories to the current pre‐forecast period. The analog model is developed on projection coefficients from a basis expansion of the soil moisture and sea surface temperature fields. Separate models are constructed for locations falling in each Iowa Crop Reporting District, and the forecasting ability of the proposed model is compared against a variety of alternative methods and metrics. Copyright © 2015 John Wiley & Sons, Ltd.

Petri-Net-Based Modeling of Human Operator's Planning for the Evaluation of Task Performance Using the Example of Air Traffic Control

With respect to planning capabilities, a model is developed to be used for the evaluation of human operator's cognitive performance. Plans are goal-directed interaction sequences cognitively simulated before appearing in dynamic human–machine systems. In this context, human operator's actions have long-term effects as well as different consequences depending on the time of their activation. The proposed model allows the evaluation of cognitive task performance by comparing implemented decisions to the available options represented by plans. In this paper, situation operator modeling is applied to describe operator's behavior and the planning process based on the system modeled using colored Petri Nets. The Petri Net is applied for the prediction of future system states. The planning model uses a set of rules that describe the normative behavior of operators as definitions of problems and actions required to avoid these. The rules are derived systematically from the operator's objectives and from the available actions. The proposed method allows the calculation and, therefore, the prediction of future states as well as possible upcoming conflicts within the system. Corresponding rules may modify and, therefore, affect the generated plan. The method is demonstrated using a simplified air traffic control simulation. This method extends earlier methods published by the authors for the determination of available options based on Petri Nets. It can be concluded that the calculation of sequences of actions for interaction with dynamic systems with constant change over time is possible and realized for the first time.

Making the most of data: An information selection and assessment framework to improve water systems operations

AbstractAdvances in Environmental monitoring systems are making a wide range of data available at increasingly higher temporal and spatial resolution. This creates an opportunity to enhance real‐time understanding of water systems conditions and to improve prediction of their future evolution, ultimately increasing our ability to make better decisions. Yet, many water systems are still operated using very simple information systems, typically based on simple statistical analysis and the operator's experience. In this work, we propose a framework to automatically select the most valuable information to inform water systems operations supported by quantitative metrics to operationally and economically assess the value of this information. The Hoa Binh reservoir in Vietnam is used to demonstrate the proposed framework in a multiobjective context, accounting for hydropower production and flood control. First, we quantify the expected value of perfect information, meaning the potential space for improvement under the assumption of exact knowledge of the future system conditions. Second, we automatically select the most valuable information that could be actually used to improve the Hoa Binh operations. Finally, we assess the economic value of sample information on the basis of the resulting policy performance. Results show that our framework successfully select information to enhance the performance of the operating policies with respect to both the competing objectives, attaining a 40% improvement close to the target trade‐off selected as potentially good compromise between hydropower production and flood control.

East to west – The optimal tilt angle and orientation of photovoltaic panels from an electricity system perspective

The integration of photovoltaic as a fluctuating renewable energy source has raised concerns about additional costs for the electricity system due to the variable nature of power output leading to more frequent and steeper ramping of conventional power plants and the need for backup capacity. One way to reduce those costs can be the variation of installation angles of PV panels at different locations to smoothen out the total production from PV in the whole system. To a certain extent steeper tilt angles can shift the production from summer months to winter months and the variation of the azimuth from east to west can partly shift production during the day increasing the production in morning or afternoon hours. However, for fixed mounted PV panels, there is one angle combination that maximizes the total output of the PV panel throughout the year and each deviation from this angle combination results in losses of total output. This paper evaluates the trade-off between annual energy losses and possible electricity generation cost reductions through adapting PV installation angles for the current electricity system and for potentially higher PV penetration levels in the future. A theoretical explanation why the annual maximum output of a PV system is not always the optimal solution from a system perspective is presented. To assess the effects of deviations from output maximizing angles at present, the wholesale market value of PV for various tilt angles and orientations in 23 regions of Austria and Germany using historic data on solar radiation and hourly electricity prices is calculated. For the analysis of future system states with potentially high shares of PV in the market, a linear optimization model representing the German and Austrian electricity wholesale market is applied. Furthermore the article provides additional insights on the market value of PV and CO2 emission reduction potential at high PV penetration levels from the model.Both, the historic analyses and the model results for Austria and Germany suggest that at present PV penetration levels, the angle combination that maximizes the output of a PV system also provides the highest spot market value and consequently minimal total system costs. For higher shares of PV in the system the model suggests deviations from those angle combinations to be optimal. For additional PV capacity of up to 40GW (72GW in total) a slight shift to easterly orientated PV modules is observed. For very high penetration levels of more than additional 100GW optimal deviation angles deviate strongly from the output maximizing angles and the model shows optimal angles to be up to 20° steeper and orientations from east to west. However, the impact on total electricity generation costs is very low even for extremely high shares of PV in the system (<1% change in total costs). It is also found that despite of optimized installation angles, the average market value of electricity production from PV drops significantly from 41€/MWh at the base year 2012 to below 24€/MWh for additional 40GW which has strong implications for the competitiveness of PV in the future. Marginal CO2 emission reductions however decline slower than the market values as PV cuts into production from coal power plants.

Market Implications and Pricing of Dynamic Reserve Policies for Systems With Renewables

Static reserve policies are used within security-constrained unit commitment (SCUC) and security constrained economic dispatch (SCED) to ensure reliability. A common policy is that 10-min reserve must exceed the largest contingency. However, this condition does not guarantee reliability because voltage and thermal limits can hinder reserve deliverability. Many operators use zonal reserve markets to ensure reserves are dispersed across the grid. Such zonal models attempt to anticipate transmission bottlenecks, which is a difficult task when the future system state is uncertain. This paper examines the market implications of dynamic reserve policies used to mitigate uncertainty from renewable resources and contingencies. We study the market implications of policies recently proposed in the literature, such as hourly zones within day-ahead SCUC and an algorithm that formally disqualifies reserves that are expected to be undeliverable. A locational reserve pricing scheme is also proposed in connection with scenario-based reserve disqualification. Analysis on the RTS-96 test case shows that dynamic zones and reserve disqualification, along with the proposed compensation scheme, help direct reserve payments toward resources that more effectively respond to contingencies.

Computational models of consumer confidence from large-scale online attention data: crowd-sourcing econometrics.

Economies are instances of complex socio-technical systems that are shaped by the interactions of large numbers of individuals. The individual behavior and decision-making of consumer agents is determined by complex psychological dynamics that include their own assessment of present and future economic conditions as well as those of others, potentially leading to feedback loops that affect the macroscopic state of the economic system. We propose that the large-scale interactions of a nation's citizens with its online resources can reveal the complex dynamics of their collective psychology, including their assessment of future system states. Here we introduce a behavioral index of Chinese Consumer Confidence (C3I) that computationally relates large-scale online search behavior recorded by Google Trends data to the macroscopic variable of consumer confidence. Our results indicate that such computational indices may reveal the components and complex dynamics of consumer psychology as a collective socio-economic phenomenon, potentially leading to improved and more refined economic forecasting.

Efficient Execution of Replicated Transportation Simulations with Uncertain Vehicle Trajectories

Abstract Many Dynamic Data-Driven Application Systems (DDDAS) use replicated simulations to project possible future system states. In many cases there are substantial similarities among these different replications. In other cases output statistics are independent of certain simulation computations. This paper explores computational methods to exploit these properties to speed up the simulation execution time. We discuss a new algorithm to speed up the execution of replicated vehicle traffic simulations, where the output statistics of interest focus on one or more attributes such as the trajectory of a certain “target” vehicle. By focusing on correctly reproducing the behavior of the target vehicle and its interaction with other modeled entities across the different replications and modifying the event handling mechanism the execution time can be reduced. A speculative execution method using a tagging mechanism allows this speedup to occur without loss of accuracy in the output statistics.

Application of fuzzy sets in decision analysis for prioritising critical energy infrastructures

This paper presents an approach for long-range decision-making in the domain of critical infrastructure using subjective information. Critical infrastructures provide goods and services essential to public wellbeing, including public safety, economic vitality, and security. Infrastructure owners, operators, and policy-makers have to make decisions related to fulfilling stakeholder needs. These decisions are routinely made in a landscape marked by ambiguity, complexity, unpredictability, and uncertainty associated with future states of infrastructure systems and their environment. In this paper, the authors suggest that fuzzy set theory could be used to enhance decision-making since it can account for unknown possible system states along any array of parameters. Specifically, this paper uses fuzzy sets to rank and prioritise three energy alternatives using five evaluation parameters and a possible infrastructure state of resiliency. A general model is developed that could be used to support a long-range decision process. The model can be adapted and deployed in other sectors in the domain of critical infrastructures.

Data assimilation techniques and modelling uncertainty in geosciences

Abstract. "You cannot step into the same river twice". Perhaps this ancient quote is the best phrase to describe the dynamic nature of the earth system. If we regard the earth as a several mixed systems, we want to know the state of the system at any time. The state could be time-evolving, complex (such as atmosphere) or simple and finding the current state requires complete knowledge of all aspects of the system. On one hand, the Measurements (in situ and satellite data) are often with errors and incomplete. On the other hand, the modelling cannot be exact; therefore, the optimal combination of the measurements with the model information is the best choice to estimate the true state of the system. Data assimilation (DA) methods are powerful tools to combine observations and a numerical model. Actually, DA is an interaction between uncertainty analysis, physical modelling and mathematical algorithms. DA improves knowledge of the past, present or future system states. DA provides a forecast the state of complex systems and better scientific understanding of calibration, validation, data errors and their probability distributions. Nowadays, the high performance and capabilities of DA have led to extensive use of it in different sciences such as meteorology, oceanography, hydrology and nuclear cores. In this paper, after a brief overview of the DA history and a comparison with conventional statistical methods, investigated the accuracy and computational efficiency of two main classical algorithms of DA involving stochastic DA (BLUE and Kalman filter) and variational DA (3D and 4D-Var), then evaluated quantification and modelling of the errors. Finally, some of DA applications in geosciences and the challenges facing the DA are discussed.

Scientific Management 2.0 – A Design Model for Experimental Research in Production Management

Reliable and accurate predictions on future states of production systems are the objective of production theories. In this paper, the authors determined shortcomings of current deterministic models and traced them back to the poor theoretical basis of scientific research in the area. The observations resulted in the development of the concept Scientific Management 2.0 as an appropriate research methodology for production management. This new empirical approach takes into account three requirements to scientifically precise investigations: It expands existing theory by socio-technical aspects, uses embedded experiments as a profound basis for investigation and provides a design that warrants the methodical exactness required. RWTH Aachen’s Demonstration Factory represents an adequate infrastructure to prove feasibility and performance of the new approach.

Fuzzy Integration of Support Vector Regression Models for Anticipatory Control of Complex Energy Systems

Anticipatory control systems are a class of systems whose decisions are based on predictions for the future state of the system under monitoring. Anticipation denotes intelligence and is an inherent property of humans that make decisions by projecting in future. Likewise, intelligent systems equipped with predictive functions may be utilized for anticipating future states of complex systems, and therefore facilitate automated control decisions. Anticipatory control of complex energy systems is paramount to their normal and safe operation. In this paper a new intelligent methodology integrating fuzzy inference with support vector regression is introduced. The proposed methodology implements an anticipatory system aiming at controlling energy systems in a robust way. Initially, a set of support vector regressors is adopted for making predictions over critical system parameters. The predicted values are used as input to a two-stage fuzzy inference system that makes decisions regarding the state of the energy system. The inference system integrates the individual predictions at its first stage, and outputs a decision together with a certainty factor computed at its second stage. The certainty factor is an index of the significance of the decision. The proposed anticipatory control system is tested on a real-world set of data obtained from a complex energy system, describing the degradation of a turbine. Results exhibit the robustness of the proposed system in controlling complex energy systems.

Discrete and Continuous, Probabilistic Anticipation for Autonomous Robots in Urban Environments

This paper develops a probabilistic anticipation algorithm for dynamic objects observed by an autonomous robot in an urban environment. Predictive Gaussian mixture models are used due to their ability to probabilistically capture continuous and discrete obstacle decisions and behaviors; the predictive system uses the probabilistic output (state estimate and covariance) of a tracking system and map of the environment to compute the probability distribution over future obstacle states for a specified anticipation horizon. A Gaussian splitting method is proposed based on the sigma-point transform and the nonlinear dynamics function, which enables increased accuracy as the number of mixands grows. An approach to caching elements of this optimal splitting method is proposed, in order to enable real-time implementation. Simulation results and evaluations on data from the research community demonstrate that the proposed algorithm can accurately anticipate the probability distributions over future states of nonlinear systems.

Online Modelling of Water Distribution System Using Data Assimilation

This paper applies Data Assimilation (DA) methods to a Water Distribution System Model to improve the real- time estimation of water demand, and hydraulic system states. A time series model is used to forecast water demands which are used to drive the hydraulic model to predict the future system state. Both water demands and water demand model parameters are corrected via DA methods to update the system state. The results indicate that DA methods improved offline hydraulic modelling predictions. Of the DA methods, the Ensemble Kalman Filter outperformed the Kalman Filter in term of updating demands and water demand model parameters.

An imperative need for global change research in tropical forests

Tropical forests play a crucial role in regulating regional and global climate dynamics, and model projections suggest that rapid climate change may result in forest dieback or savannization. However, these predictions are largely based on results from leaf-level studies. How tropical forests respond and feedback to climate change is largely unknown at the ecosystem level. Several complementary approaches have been used to evaluate the effects of climate change on tropical forests, but the results are conflicting, largely due to confounding effects of multiple factors. Although altered precipitation and nitrogen deposition experiments have been conducted in tropical forests, large-scale warming and elevated carbon dioxide (CO2) manipulations are completely lacking, leaving many hypotheses and model predictions untested. Ecosystem-scale experiments to manipulate temperature and CO2 concentration individually or in combination are thus urgently needed to examine their main and interactive effects on tropical forests. Such experiments will provide indispensable data and help gain essential knowledge on biogeochemical, hydrological and biophysical responses and feedbacks of tropical forests to climate change. These datasets can also inform regional and global models for predicting future states of tropical forests and climate systems. The success of such large-scale experiments in natural tropical forests will require an international framework to coordinate collaboration so as to meet the challenges in cost, technological infrastructure and scientific endeavor.

When relationships estimated in the past cannot be used to predict the future: using mechanistic models to predict landscape ecological dynamics in a changing world

Researchers and natural resource managers need predictions of how multiple global changes (e.g., climate change, rising levels of air pollutants, exotic invasions) will affect landscape composition and ecosystem function. Ecological predictive models used for this purpose are constructed using either a mechanistic (process-based) or a phenomenological (empirical) approach, or combination. Given the accelerating pace of global changes, it is becoming increasingly difficult to trust future projections made by phenomenological models estimated under past conditions. Using forest landscape models as an example, I review current modeling approaches and propose principles for developing the next generation of landscape models. First, modelers should increase the use of mechanistic components based on appropriately scaled “first principles” even though such an approach is not without cost and limitations. Second, the interaction of processes within a model should be designed to minimize a priori constraints on process interactions and mimic how interactions play out in real life. Third, when a model is expected to make accurate projections of future system states it must include all of the major ecological processes that structure the system. A completely mechanistic approach to the molecular level is not tractable or desirable at landscape scales. I submit that the best solution is to blend mechanistic and phenomenological approaches in a way that maximizes the use of mechanisms where novel driver conditions are expected while keeping the model tractable. There may be other ways. I challenge landscape ecosystem modelers to seek new ways to make their models more robust to the multiple global changes occurring today.

Adaptive Neural Network Generalized Predictive Control for Unknown Nonlinear System

This paper presents an adaptive neural control design for a class of unknown nonlinear systems. Novel state variables and the corresponding transform are introduced, such that the state-feedback control of a pure-feedback system can be viewed as the output-feedback control of a canonical system. An adaptive predictor incorporated with a neural network observer is proposed to obtain the future system states predictions, which are used in the control design to circumvent the input delay and nonlinearities. The proposed predictor, observer and controller are all online implemented, and the closed-loop system stability is guaranteed. The conventional backstepping design and analysis for pure-feedback systems are avoided, which renders the developed scheme simpler in its synthesis and application. Practical guidelines on the control implementation and the parameter design are provided. The applicability in nonlinear system is demonstrated by simulation experiments. DOI: http://dx.doi.org/10.11591/telkomnika.v11i7.2804 Full Text: PDF

Simplified hydraulic modelling in model predictive control of flood mitigation measures along rivers

Model predictive control (MPC) is an advanced technique for controlling water resources systems over short-term prediction horizons by optimizing control trajectories of hydraulic structures. Future system states are forecasted by an internal model that simulates the system response to a set of external disturbances and control inputs. This paper deals with the design and numerical implementation of dedicated simplified hydraulic models for representing flood routing processes in MPC. We present alternatives for the numerical implementation in simulation mode as well as first-order sensitivity mode and discuss the trade-off between model accuracy and computational performance in sequential and simultaneous MPC set-ups in application to two test cases: The first one is a fictitious estuary case with little potential for simplifying the full dynamic model. The second case is an application of MPC to the control of flood detention measures along the Rhine River in Germany. Both cases show the high potential ...