Performance Forecasting Research Articles

A better project performance prediction model using fuzzy time series and data envelopment analysis

Earned value management (EVM) is a critical project management methodology that evaluates and predicts project performance from cost and schedule perspectives. The novel theoretical framework presented in this paper estimates future performance of a project based on the past performance data. The model benefits from a fuzzy time series forecasting model in the estimation process. Furthermore, fuzzy-based estimation is developed using linguistic terms to interpret different possible conditions of projects. Eventually, data envelopment analysis is applied to determine the superior model for forecasting of project performance. Multiple illustrative cases and simulated data have been used for comparative analysis and to illustrate the applicability of theoretical model to real situations. Contrary to EVM-based approach, which assumes the future performance is the same as the past, the proposed model can greatly assist project managers in more realistically assessing prospective performance of projects and thereby taking necessary and on-time appropriate actions.

Forecasting Destination Weekly Hotel Occupancy with Big Data

Hospitality constituencies need accurate forecasting of future performance of hotels in specific destinations to benchmark their properties and better optimize operations. As competition increases, hotel managers have urgent need for accurate short-term forecasts. In this study, time-series models incorporating several tourism big data sources, including search engine queries, website traffic, and weekly weather information, are tested in order to construct an accurate forecasting model of weekly hotel occupancy for a destination. The results show the superiority of ARMAX models with both search engine queries and website traffic data in accurate forecasting. Also, the results suggest that weekly dummies are superior to Fourier terms in capturing the hotel seasonality. The limitations of the inclusion of multiple big data sources are noted since the reduction in forecasting error is minimal.

도심 빌딩 옥상에 적용 가능한 풍력발전시스템의 성능 평가 연구

This study had the purpose on feasibility judgment through performance forecast of wind power generation system using the cross flow vertical type wind power turbine for the situation of domestic small size wind power technology development. Wind power generation system uses the principle of venturi tube that gathers the wind through the first guide vane, and second guide vein changes the angle of the wind simultaneously by playing the role of venturi tube. After this, wind got out from the second guide vane spins the wind power turbine and has the meaning of judging on the aspect of numerical interpretation the feasibility for the small size wind power generation through wind power generation system that comes out from the back.

Forecasting Techniques and Accuracy of Performance Forecasting

This article explores the impact of the different forecasting methods (FMs) on the accuracy of performance forecasting (APF) in large manufacturing firms (LMFs), in Kenya. The objective of the study was to assess if the different forecasting methods have an influence on any of the aspects of measures of APF. APF, in manufacturing operations, is seldom derived accurately. However, LMFs tend to hire skilled forecasters, to a great extent, to ensure APF when preparing future budgets. The different types of forecasting techniques have been known to influence the behavior of operations resulting in the formulation of either accurate or inaccurate forecasts resulting in either adverse or favorable organizational performance. The study used the three known forecasting methods, objective, subjective and combined forecasting techniques against measures of APF, expected value, growth in market share, return on assets and return on sales. Regression analysis was used applying data collected through a structured questionnaire administered among randomly selected LMFs. Results indicated that there was evidence that APF is influenced by each of the forecasting methods in different ways.

Forecasting Techniques and Accuracy of Performance Forecasting

This article explores the impact of the different forecasting methods (FMs) on the accuracy of performance forecasting (APF) in large manufacturing firms (LMFs), in Kenya. The objective of the study was to assess if the different forecasting methods have an influence on any of the aspects of measures of APF. APF, in manufacturing operations, is seldom derived accurately. However, LMFs tend to hire skilled forecasters, to a great extent, to ensure APF when preparing future budgets. The different types of forecasting techniques have been known to influence the behavior of operations resulting in the formulation of either accurate or inaccurate forecasts resulting in either adverse or favorable organizational performance. The study used the three known forecasting methods, objective, subjective and combined forecasting techniques against measures of APF, expected value, growth in market share, return on assets and return on sales. Regression analysis was used applying data collected through a structured questionnaire administered among randomly selected LMFs. Results indicated that there was evidence that APF is influenced by each of the forecasting methods in different ways.

Forecasting Techniques, External Operating Environment and Accuracy of Performance Forecasting

This article explores the influence of the external operating environment (EOE) on the accuracy of performance forecasting (APF) in large manufacturing firms (LMFs) in Kenya. The objective was to assess if any aspects of the EOE influenced measures of APF. APF, in manufacturing operations, is seldom derived accurately. However, since LMFs tend to hire skilled personnel, this survey presumes that these qualified forecasters ensure APF when preparing future budgets. EOE has been known to influence the behavior of operations resulting in either adverse or favorable organizational performance. The study identified EOE factors that impact organizational performance and tested these against measures of APF. Regression analysis was applied using data collected through a structured questionnaire administered among randomly selected LMFs. Results indicated that there was evidence that EOE had an influence on some measures of APF and the established forecasting techniques (FTs), objective, judgmental and combined forecasting.

Influence of magnitude and permeability of fracture networks on behaviors of vertical shale gas wells by a free-simulator approach

After stimulated reservoir volume (SRV), complex fracture networks can be generated around shale gas (SG) vertical wells. Due to intricate flow characteristics, performance forecasts of those wells highly depend on numerical simulators. To lighten the burden of numerical computation work, a free-simulator approach was proposed in this work.To facilitate methodology development, the SG formation was assumed to be made up of two systems (reservoir/fracture system) and four media (shale matrix, unreacted nature fractures, reacted nature fractures or micro fractures (MF), and hydraulic fractures (HF)). Based on that, a mathematical model comprised of reservoir equations, fracture equations, and mass balance equations was developed for vertically fracture-network wells (VFNW). Using Laplace transformation and Pedrosa's substitution, equations in the mathematical model were solved. Type curves were generated and flow regimes were identified. Case study and sensitivity analysis were also conducted.Results from this study show that the possible flow regimes for a VFNW in SG reservoirs includes (1) wellbore storage and skin effect, (2) bilinear flow, (3) formation linear flow, (4) pseudo boundary-dominated flow, (5) bi-radial flow, (6) Knudsen diffusion flow, and (7) pseudo radial flow. As the magnitude of fracture networks increases, the formation linear flow lasts longer and the pseudo boundary-dominated flow occurs later. As the permeability of fracture networks increases, the pseudo boundary-dominated flow becomes more obvious. Additionally, it is found that pressure depletion in the SG reservoir decreases with the increase of magnitude or permeability of fracture networks.This work provides a novel respective on performance forecast on vertical wells with fracture networks, which has considerable significances on fracturing optimization as well as productivity estimation for SRV.

Predicted and actual indoor environmental quality: Verification of occupants’ behaviour models in residential buildings

Occupants’ interactions with the building envelope and building systems can have a large impact on the indoor environment and energy consumption in a building. As a consequence, any realistic forecast of building performance must include realistic models of the occupants’ interactions with the building controls (windows, thermostats, solar shading etc.).During the last decade, studies about stochastic models of occupants’ behaviour in relation to control of the indoor environment have been published. Often the overall aim of these models is to enable more reliable predictions of building performance using building energy performance simulations (BEPS). However, the validity of these models has only been sparsely tested.In this paper, stochastic models of occupants’ behaviour from literature were tested against measurements in five apartments. In a monitoring campaign, measurements of indoor temperature, relative humidity and CO2 concentration was measured in the living room and bedroom at five minute intervals in five apartments with similar layout in a building located in Copenhagen, Denmark. Outdoor temperature, relative humidity, wind speed and solar radiation were obtained from a weather station close by.The stochastic models of window opening and heating set-point adjustments were implemented in the BEPS tool IDA ICE. Two apartments from the monitoring campaign were simulated using the implemented models and the measured weather data. The results were compared to measurements from the monitoring campaign to get an estimate of the forecast’s realism.The simulations resulted in realistic predictions in a sense that the measured values were within or close to the range of the simulated values. The variation in the simulated and measured variables between apartments and over time was similar. However, comparisons of the average stochastic predictions with the measured temperatures, relative humidity and CO2 concentrations revealed that the models did not predict the actual indoor environmental conditions well.

Integration of incremental filter-wrapper selection strategy with artificial intelligence for enterprise risk management

The deterioration in enterprises’ profitability not only threatens the interests of those firms, but also means related parties (investors, bankers, and stakeholders) could encounter tremendous financial losses, which could also impact the circulation of limited economic resources. Thus, an enterprise risk forecasting mechanism is urgently needed to assist decision-makers in adjusting their operating strategies so as to survive under any highly turbulent economic climate. This research introduces a novel hybrid model that incorporates an incremental filter-wrapper feature subset selection with the statistical examination and twin support vector machine (IFWTSVM) for enterprise operating performance forecasting. To promote a hybrid model’s real-life application, the knowledge visualization extracted from IFWTSVM is represented in an easy-to-grasp style. The experimental results reveal that IFWTSVM’s forecasting quality is very promising for financial risk mining, relative to other forecasting techniques examined in this study.

Modeling and performance prediction of chromate reduction by iron oxide coated sand in adsorber reactors

Iron oxide coated sand (IOCS) as an adsorbent medium for removing hexavalent chromium (Cr(VI)) from industrial wastewater, as well as in permeable reactive barriers for remediation of Cr(VI) in aquifers is investigated in this study. An important feature was the use of a mathematical model for performance forecasting and process upscaling of IOCS fixed‐bed adsorber systems for Cr(VI) removal. Another significance aspect was the elucidation of IOCS surface mechanisms and interactions responsible for Cr(VI) sorption and reduction to the less toxic Cr(III). The adsorption equilibrium and mass‐transfer parameters for modeling were obtained from independent laboratory studies. Adsorber studies validated the predictive model and established the effectiveness of IOCS for Cr(VI) removal under different conditions. Model simulation studies demonstrated that adsorbent capacity, surface diffusion, and film transfer significantly influenced process dynamics. The study showed that IOCS can be used to remove Cr(VI) from contaminated waters, meeting the overall objectives of regulatory agencies. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3717–3729, 2016

Hybrid Kernelized Fuzzy Clustering and Multiple Attributes Decision Analysis for Corporate Risk Management

This study introduces an emerging risk management architecture by extending balanced scorecards (BSC) with risk exposure considerations for corporate operating performance assessment and then constructs a hybrid mechanism that combines kernelized fuzzy C-means (KFCM), multiple attributes decision analysis (MADA), and extreme learning machine (ELM) for corporate operating performance forecasting. KFCM is implemented to do the clustering task for each corporate under each aspect of BSC. No specific corporate reaches optimal performance under each assessing measure—that is, dissimilar assessing criteria leads to dissimilar outcomes. This method can be transformed into a MADA task and a MADA algorithm that can yield a reliable outcome systematically. Sequentially, the outcome is fed into ELM to construct the performance forecasting mechanism. The introduced mechanism with outstanding forecasting performance comes with a critical challenge: it lacks interpretability, which impedes its real-life usage. To cope with this problem, the rough set theory (RST) is employed to extract the inherent decision logics from the black-box model and visualize it in human readable formats. The introduced model has been examined by real cases and is a promising alternative for corporate risk management.

A Semianalytical Approach for Obtaining Type Curves of Multiple-Fractured Horizontal Wells With Secondary-Fracture Networks

Summary In naturally fractured reservoirs, complex fracture systems can easily develop along a horizontal wellbore during hydraulic fracturing. In the fracture systems, multiple, discrete secondary fractures are connected to the multiple-fractured horizontal well (MFHW). Because of the fracture complexity, most studies about performance forecast of such MFHWs highly depend on numerical simulators. In this paper, a new semianalytical approach is proposed to overcome the challenge to analyze the pressure behavior of MFHWs in complex-fracture systems. First, a mathematical model for MFHWs with secondary-fracture networks is established. Then, with Gauss elimination and the Stehfest numerical algorithm (Stehfest 1970), the transient-pressure solution of the mathematical model is solved, and type curves of MFHWs with secondary-fracture networks are obtained. After that, model validation and sensitivity analysis are conducted. It is found that the presented approach can rapidly and accurately generate type curves of MFHWs with secondary-fracture networks. This work provides very meaningful references for reservoir engineers in fracturing evaluations as well as performance estimations of MFHWs in naturally fractured reservoirs.

OPTIMIZATION SYSTEM FOR MONITORING AND FORECAST OF HEALTH STUDENTS

This article listed the data on the improvement and expansion of the landmark phase prediction system of formation of health of adolescent population, depending on dietary habits. Step prediction is based on the results of the determination of the cluster analysis of physical development and nutritional factors in age-sex groups of teenagers and use of predictive equations. With the help of data analysis methods (factor, multivariate regression analysis) were the basic anthropometric features identified and explored their relationship to power adolescents 10–17 years. The research allowed by a selected procedure of cluster analysis divided all statistical sample into 4 clusters for girls and 3 for boys cluster. For each of the clusters were built complexes predictive equations that using tables, allow the doctor to make a forecast of physical development performance for a particular child.

A practical workflow for characterizing stress-dependent behaviour of coal from changes in well productivity

Stress (or pressure) dependence of coal permeability is a commonly observed and generally accepted dynamic behaviour that is often ignored from production performance forecasting. Reasons for this omission typically include (a) the difficulties in reliably characterizing stress dependent effects from a limited number of pressure buildup (PBU) tests, and (b) large uncertainties in our understanding of both the porosity and compressibility of coals. This paper demonstrates a new analytical workflow, and proposes a new set of equations that overcomes some of those limitations. Engineers can use this paper to analytically translate expected permeability changes with pressure, to productivity changes with pressure differences.This paper utilizes a slightly modified form of Palmer-Mansoori (P&M) model in a workflow that includes. 1Estimation of coal cleat volume compressibility using permeability-depth trends. 2Characterization of mechanical skins from interpreted apparent skins. 3Calculation of stress dependent pseudo pressure (SDPP) – converting permeability changes with pressure to productivity changes with pressure differences – enabling the use of well productivities over time as part of a SDP characterization process. 4The matching through regression of relative changes in well productivity indices in groups of wells - utilizing the SDPP approach – with a single stress dependent controlling parameter, in a way that is suited to extrapolating away from well control.Theoretical support for this approach is provided via derivations from published models. A methodology is outlined – sharing the results of a field example – to demonstrate the relative ease with which the analytical process can be applied. Further, pitfalls are highlighted of using well productivities as a direct proxy for permeability changes, or even utilizing coarse grid numerical simulation in the matching process. Finally, further applications and limitations of its application are also discussed.This paper addresses existing knowledge gaps in the coal bed methane (CBM) industry by providing a simple, yet efficient, workflow for characterizing and incorporating stress-dependence of permeability in CBM Reservoir Engineering. This is achieved through the application of new analytical equations to characterize stress dependent pseudo pressure, enabling the direct use of well productivity changes, and can be used standalone, or as a means to accelerate a numerical history matching workflow.

Incorporated risk metrics and hybrid AI techniques for risk management

This study proposes a novel technique by extending balanced scorecards with risk management considerations (i.e., risk metrics and insolvency risk) for corporate operating performance assessment and then establishes a fusion mechanism that incorporates hybrid filter-wrapper subset selection (HFW), random vector functional-link network (RVFLN), and ant colony optimization (ACO) for operating performance forecasting. The study executes HFW, which preserves the advantages of wrapper approaches, but prevents paying its tremendous computational cost, in order to determine the essential features for forecasting model construction. With the merits of rapid learning speed and no extra inherent parameters needed to be tuned, RVFLN helps establish the forecasting model. However, RVFLN has demonstrated that its superior forecasting performance comes with the challenge of being unable to represent the inherent decision logic for humans to comprehend. To cope with this task, the study conducts ACO so as to extract the inherent knowledge from RVFLN and represents it in human-readable format. If the extracted knowledge is not comprehensive for decision makers, then they will not be able to interpret and verify it. In this circumstance, the decision makers probably will not trust enough the extracted knowledge and be prone to making unreliable judgments more easily. The introduced mechanism herein is examined by real cases and poses superior forecasting quality under numerous examinations. It is a promising alternative for corporate operating performance forecasting.

Earnings Pressure and Long-Term Corporate Governance: Can Long-Term-Oriented Investors and Managers Reduce the Quarterly Earnings Obsession?

Recent research has shown that managers in publicly traded companies facing earnings pressure—the pressure to meet or beat securities analysts’ earnings forecasts—may make business decisions to improve short-term earnings. Analysts’ forward-looking performance forecasts can serve as powerful motivation for managers, but may also encourage them to undertake short-term actions detrimental to future competitiveness and performance. To identify whether managerial reactions to earnings pressure suggest evidence of intertemporal trade-offs, we explored how companies respond to earnings pressure under different conditions of corporate governance that shape the temporal orientations of managers. Using data on competitive decisions made by U.S. airlines under quarterly earnings pressure, we examined the effect of earnings pressure on competitive behavior under different ownership structures (ownership by long-term dedicated investors versus transient investors) and CEO incentives (unvested incentives that are restricted or unexercisable in the short term, versus vested incentives). The results suggest that companies with more long-term-oriented investors and long-term-aligned CEOs with unvested incentives are less likely to soften competitive behavior in response to earnings pressure, relative to companies with transient investors and CEOs with vested, immediately exercisable stock-based incentives. Using a difference-in-differences (DiD) specification for stronger identification, we also found that firms respond to their rivals’ earnings pressure shocks by increasing capacity and prices, particularly when those rivals do not have long-term-oriented investors and CEO incentives. The evidence is more aligned with the view that the pursuit of short-term earnings as a result of earnings pressure may be detrimental to long-term competitiveness.

Diffusion coefficients of supercritical CO2 in oil-saturated cores under low permeability reservoir conditions

CO2 diffusion in oil-saturated porous media with low permeability is of great importance for the project design, risk assessment, and performance forecast of carbon capture and storage (CCS) or enhanced oil recovery (EOR). This paper developed a method to determine CO2 diffusion in oil-saturated cores under low permeability reservoir conditions. Core, crude oil and experimental parameters were taken from the representative low permeability reservoir. In the solution of the mathematical model, oil saturation was introduced in to diffusion equation, an oil-phase swelling caused by gas dissolution was considered, but a water-phase swelling was not, which is in agreement with the actual diffusion situation. The error caused by the state equation of carbon dioxide was eliminated, improving the calculation accuracy of the diffusion coefficient. The effects of pressure (6.490–29.940MPa), temperature (70–150°C), oil saturation (0–63.58%) and permeability (8.62–985.06mD) on the diffusion coefficient of supercritical CO2 in low-permeability reservoirs were studied. The order of the diffusion coefficient is from 10−10 to 10−9m2/s. The results show that with an increase in pressure and temperature, the CO2 diffusion coefficient in the porous media saturated with oil firstly increases significantly and then the rate of increase gradually slowed down. The CO2 diffusion coefficient increases greatly with the oil saturation in porous media. The CO2 diffusion coefficient first increases greatly with permeability, and when the permeability of the core is greater than approximately 100mD, it remains almost stable. The experimental results can provide theoretical support for CO2 transport in porous media.

Probabilistic Reservoir-Property Modeling Jointly Constrained by 3D-Seismic Data and Hydraulic-Unit Analysis

SummaryWe present a new method for seismic reservoir characterization and reservoir-property modeling on the basis of an integrated analysis of 3D-seismic data and hydraulic flow units, and apply it to an example of a producing reservoir offshore Western Australia. Our method combines hydraulic-unit analysis with a set of techniques for seismic reservoir characterization including rock-physics analysis, Bayesian inference, prestack seismic inversion, and geostatistical simulation of reservoir properties. Hydraulic units are geologic layers and zones characterized by similar properties of fluid flow in porous permeable media, and are defined at well locations on the basis of logs, core measurements, and production data. However, the number of wells available for hydraulic-unit analysis is often extremely limited. In comparison, the lateral coverage and resolution of 3D-seismic data are excellent, and can thus be used to extend hydraulic-unit analysis away from well locations into the full 3D reservoir volume. We develop a probabilistic relationship between optimal 3D-seismic-data attributes and the hydraulic units that we determine at well locations. Because porosity and permeability distributions are estimated for each hydraulic flow unit as part of the process, we can use the 3D-seismic probabilistic relationships to constrain geostatistical realizations of porosity and permeability in the reservoir, to be consistent with the flow-unit analysis. Reservoir models jointly constrained by both 3D-seismic data and hydraulic flow-unit analysis have the potential to improve the processes of reservoir characterization, fluid-flow performance forecasting, and production data or 4D-seismic history matching.

Rate forecasting during boundary-dominated multiphase flow: The rescaled exponential model

Abstract Well performance forecasting is an important analytical technique used for field development to guide economic decisions during the life of a reservoir. For the case of dry and liquid-rich gas wells, traditional well performance models are developed based on solving the resultant highly nonlinear gas flow equations via pseudo-pressure and pseudo-time linearization. In this study, we provide a straightforward, density-based alternative to traditional models. We show, as done previously for the case of dry gas wells ( Ayala H. and Zhang, 2013 , Zhang and Ayala, 2014a ), that a rescaled exponential model is a rigorous decline solution that can be extended to liquid-rich gas wells producing under constant bottomhole-flowing-pressure (BHP) during boundary-dominated flow (BDF) and multiphase conditions. The proposed multiphase rescaled-exponential model is derived analytically from governing multiphase flow equations; comparisons between numerically simulated results and proposed analytical model for a variety of combinations of reservoir and fluid properties demonstrate that the proposed rescaled-exponential model is a valid and reliable forecast model for constant-BHP liquid-rich gas wells under BDF.

Comparative Analysis between Single Diode and Double Diode Model of PV Cell: Concentrate Different Parameters Effect on Its Efficiency

This research appraises comparative analysis between single diode and double diode model of photovoltaic (PV) solar cells to enhance the conversion efficiency of power engendering PV solar systems. Single diode model is simple and easy to implement, whereas double diode model has better accuracy which acquiesces for more precise forecast of PV systems performance. Exploration is done on the basis of simulation results and MATLAB tool is used to serve this purpose. Simulations are performed by varying distinct model parameters such as solar irradiance, temperature, value of parasitic resistances, ideality factor of diode and number of series and parallel connected solar cells used to assemble PV array. Conspicuous demonstration is executed to analyze effects of these specifications on the efficiency curve and power vs. voltage output characteristics of PV cell for specified models.