Performance Forecasting Research Articles

ANN modeling for forecasting of VCR engine performance and emission parameters fuelled with green diesel extracted from waste biomass resources.

In this research work, the experimental tests were conducted on a single-cylinder, constant speed, variable compression ratio (VCR) engine fuelled with green diesel. Initially, bio-oil was extracted from waste Trichosanthes cucumerina fruit seeds using the Soxhlet apparatus. The acquired bio-oil is used to make green diesel through the trans-esterification process. The fuel blends were prepared with different proportions of Trichosanthes cucumerina biodiesel (TCB) in diesel fuel (30%, 50%, and 70%) for the experimental test, and their thermo-physical properties were evaluated according to ASTM standards. At full load condition, the TCB30 blend with CR 18:1 gives closer engine performance of brake thermal efficiency (33.52%), brake specific fuel consumption (0.27kg/kWh), and exhaust gas temperature (389.56°C) and reduced emission levels of unburned hydrocarbon by 13.51%, carbon monoxide by 10.82%, smoke opacity by 16.87%, and the penalty of nitric oxide by 17.56% equated with neat diesel fuel. The engine performance and emission parameters are predicted using multiple regression artificial neural network (ANN) models. A database generated from the experimental results is used to train the ANN model. The average correlation coefficient (R) of the trained ANN model is 0.99673, which is closer to 1. It indicates that the proposed ANN model can generate the exact correlation between input factors and output responses. As a result, the application of ANN is a better forecasting tool for predicting VCR engine performance and emission characteristics.

Building-Integrated Photovoltaic (BIPV) products and systems: A review of energy-related behavior

This paper reviews the main energy-related features of building-integrated photovoltaic (BIPV) modules and systems, to serve as a reference for researchers, architects, BIPV manufacturers, and BIPV designers. The energy-related behavior of BIPV modules includes thermal, solar, optical and electrical aspects. Suitable standardization to evaluate heat transfer and solar heat gain by BIPV modules still need to be developed further since BIPV elements behave differently to the building elements they substitute. The optical properties of BIPV modules, such as light transmittance or color rendering, also play a role in the search for a good balance between energy saving, electricity generation, aesthetics and visual comfort. However, architecturally adapted BIPV design may affect the electrical performance also, by reducing the efficiency of BIPV modules and systems compared to standard photovoltaic (PV) ones. This is not the sole challenge for the electrical designers, as the special operating conditions of BIPV systems such as non-homogeneous irradiance complicate the electrical design and the forecasting of BIPV performance. The aim of this review is to present the current state of knowledge of the aspects mentioned above, to promote continued progress in BIPV and to inform suitable standardization efforts.

Forecasting the medium-term performance of restructured tourism firms with an adaptive integrated predictor

This paper investigates the forecasting of the medium-term performance of restructured tourism firms using a new adaptive integrated predictor. The predictor incorporates internal characteristics of firms (economic factors and management practice), restructuring characteristics (strategy types and payment means), and timing (e.g. calendar month) of the restructuring. The results show that, the firms that perform better after restructuring are those that: a) have experience of operating under liability, b) are able to acquire large amounts of undistributed profit and liquid assets, and c) are able to increase their turnover. Tourism firms that generate large proportion of profit with their equity value and rapid growth in their operational income will suffer losses due to restructuring. Forecasting the medium-term performance of restructured tourism firms needs to take account of time heterogeneity derived from the periodicity (seasonality) inherent to the tourism industry. The adaptive integrated predictor presented in this paper performs better than a series of benchmark models, because it is able to allow for both the high-risk nature and periodicity of the tourism industry.

Optimization and Performance Analysis of Residential Building for Sustainable Energy Design Through BIM

The problems and effects of global warming stand a primary concern for most of the people in today’s world. As the demand for energy increases, the available energy resources noticeably decrease which rapidly upsurges the need for energy-efficient buildings. Housing is one of the largest energy consumers as it consumes about 40% of the total energy. This study aims to improve the energy consumption and the strength of houses in India compared to other materials in construction walls. The study includes the record of electricity life cycle, fuel consumption and Life Cycle Energy Costs (LCEC), the intensity of the annual energy consumption and the peak value of the annual demand, its evaluation and presents the comparison with the original planning. Finally, the measure of the total annual energy and total electricity and fuel life cycle costs for the building wall in the Indian scenario using sustainable energy efficient design and construction methods under BIM and tools like Revit, Insight360 and Green Building Studio are presented. An energy analysis model that integrates BIM to create precise residential performance forecasts with better optimization scenarios is represented.

Business Processes in the Transport and Logistics in Economic Operation of the Enterprise

Because the transport and logistics system (TLS) of the business enterprise is exposed to obvious and hidden high risks for the owners, its business processes need effective management throughout the value added chain. The purpose of the article is to present theoretical and methodological justification of TLS operation at enterprise level from the perspective of integrated management of business processes involved in the transport and logistics services.
 The article focuses on elaborating and constructing a diagram of the three-vector approach to building up TLS in the business enterprise, based on the compliance with the regulatory norms (national and international regulatory framework, intramural regulations, compliance with contract terms), risk orientation (economic forecasting of TLS performance, optimization of TLS capacities, analysis of contracting parties operation, computerization of business processes, economic projecting and strategic management), and client orientation (high quality of the provided services, infrastructure improvements, free competition, effective marketing, rationalization of supply chains, provision of guarantees; price policy). It is proposed to group the key business processes involved in TLS by transport and logistics activities, with account to the activity specifics and size of the enterprise, terms of service delivery and other factors of value added creation. A set of indicators for performance assessment of TLS is constructed, classified in the following groups: quality of transport services, efficiency of freight transport, business indicators of diagnostics of the transport activities as a component of the logistics system, assessment of logistics rules, transport efficiency. These indicators will provide an effective tool for performance assessment of TLS at enterprise level.

A Full-Spectrum Spectral Imaging System Analytical Model With LWIR TES Capability

With the popularity of (hyperspectral) remote sensing systems coupled with a myriad of applications, comes the need for investigations into hyperspectral system designs and parameter trade-off studies. Analytical models based on statistical descriptions and signal propagation are efficient methods to examine these parameter trade-off studies, as well as sensitivity studies, with low computational cost. In this paper, a newly developed long wave infrared (LWIR) statistical iterative spectrally smooth temperature/emissivity separation (S-ISSTES) algorithm has been integrated into a widely used full spectrum hyperspectral remote sensing system model known as the forecasting and analysis of spectroradiometric system performance (FASSP) model. This new tool now allows users to perform <i>LWIR</i> full system (<i>i.e</i>., from surface reflectance, to sensor, to retrieve emissivity, to detection analysis) trade studies. In this paper, we validate the LWIR model and detection performance of the new FASSP model followed by illustrating the usage of the full system model by demonstrating trade examples including useful parameter trade studies and subpixel detection sensitivity studies.

A High-Efficiency Inversion Method for the Material Parameters of an Alberich-Type Sound Absorption Coating Based on a Deep Learning Model

Research on the acoustic performance of an anechoic coating composed of cavities in a viscoelastic material has recently become an area of great interest. Traditional forward research methods are unable to manipulate sound waves accurately and effectively, are difficult to analyse, have time-consuming solution processes, and have large optimization search spaces. To address these issues, this paper proposes a deep learning-based inverse research method to efficiently invert the material parameters of Alberich-type sound absorption coatings and rapidly predict their acoustic performance. First, an autoencoder (AE) model is pretrained to reconstruct the viscoelastic material parameters of an Alberich-type sound absorption coating, the material parameters are extracted into the latent feature space by the encoder, and the decoder model is saved. The internal relationship between the reflection coefficient and latent feature space is trained to establish a multilayer perceptron (MLP). Then, the reflection coefficients in the test set are input to the trained MLP and decoder models to automatically invert the material parameters. The accuracy of the inversion result is 95.08%. Finally, a predictive model is trained to rapidly predict the acoustic performance of the inverted material parameters. The speed of a single test target is 80 times faster than that of the finite element method (FEM). Furthermore, sound absorber material parameters with the best sound absorption performance and a three-band sound absorber are inverted, and their actual sound absorption performance is predicted by the proposed method. The proposed deep learning-based inversion research method provides a solution for low-frequency, wide-band, strong attenuation, and precisely controlled sound waves. It achieves an efficient inversion of material parameters and the rapid forecasting of acoustic performance. The training model can be used for a sound absorbing coating composed of irregular cavities in a viscoelastic material and predict its acoustic performance by only modifying the dataset.

Cost–Benefit Analysis of a Trans-Arctic Alternative Route to the Suez Canal: A Method Based on High-Fidelity Ship Performance, Weather and Ice Forecast Models

Cost–Benefit Analysis of a Trans-Arctic Alternative Route to the Suez Canal: A Method Based on High-Fidelity Ship Performance, Weather and Ice Forecast Models

Cost–Benefit Analysis of a Trans-Arctic Alternative Route to the Suez Canal: A Method Based on High-Fidelity Ship Performance, Weather and Ice Forecast Models

Cost–Benefit Analysis of a Trans-Arctic Alternative Route to the Suez Canal: A Method Based on High-Fidelity Ship Performance, Weather and Ice Forecast Models

Prophet model in financial performance forecast: implementation in manufacturing sector

Prophet model in financial performance forecast: implementation in manufacturing sector

Experimental justification of directions for improving the methodological support of professional psychological selection of candidates for training in universities of the ministry of defense of the Russian Federation

This experimental study aimed to determine the possibility to increase the effectiveness of predicting the success of training and military-professional adaptation by applicants to universities of the Russian Federation Ministry of Defense by including professional psychological selection, an indicator of previously unused psychological survey methods and complex visual-motor reaction. Therefore, we examined the cadets of the VVI courses and used the results of the same groups of cadets when they pass the professional psychological selection. Mathematical and statistical data processing was carried out by discriminant analysis. The long-term forecast of successful training according to the professional psychological selection indicators is ineffective, and 61.7% of cadets have high academic performance. The inclusion of complex visual-motor reaction indicators significantly increases the reliability of the long-term forecast of high academic performance to 85.7%, overcoming the critical threshold of 75%. Additionally, by the end of the training, 10%12% of cadets developed chronic stress-induced somatic diseases, such as circulatory system (I00I99 according to the International Statistical Classification of Diseases and Related Health Problem 10th revision) and digestive system diseases (K00K93), indicating imperfect military-professional adaptation and reducing the professional activity reliability. Adding the study of complex visual-motor reaction indicators and a method that evaluates the neurotic manifestations to the existing tests in the professional psychological selection procedure is necessary to predict the propensity to stress-induced somatic diseases. The indicators in the prediction models of academic performance and propensity to stress-induced somatic diseases are different, which reflects the significant difference in their biopsychosocial mechanisms. Placing a double filter in the decisive selection rule is necessary to identify people with a forecast of high academic performance and identify stress-resistant people who are not prone to stress-induced somatic diseases among them. Developing weighting coefficients of every test block is necessary to conclude; however, the existing approach is assumed to be equivalent, that is, at variance with the complex biopsychosocial mechanism of professional reliability.

MATHEMATICAL MODELLING AS AN ELEMENT OF PLANNING RAIL TRANSPORT STRATEGIES

Effective planning and optimization of rail transport operations depends on effective and reliable forecasting of demand. The results of transport performance forecasts usually differ from measured values because the mathematical models used are inadequate. In response to this applicative need, we report the results of a study whose goal was to develop, on the basis of historical data, an effective mathematical model of rail passenger transport performance that would allow to make reliable forecasts of future demand for this service. Several models dedicated to this type of empirical data were proposed and selection criteria were established. The models used in the study are: the seasonal naive model, the Exponential Smoothing (ETS) model, the exponential smoothing state space model with Box–Cox transformation, ARMA errors, trigonometric trend and seasonal components (TBATS) model, and the AutoRegressive Integrated Moving Average (ARIMA) model. The proposed time series identification and forecasting methods are dedicated to the processing of time series data with trend and seasonality. Then, the best model was identified and its accuracy and effectiveness were assessed. It was noticed that investigated time series is characterized by strong seasonality and an upward trend. This information is important for planning a development strategy for rail passenger transport, because it shows that additional investments and engagement in the development of both transport infrastructure and superstructure are required to meet the existing demand. Finally, a forecast of transport performance in sequential periods of time was presented. Such forecast may significantly improve the system of scheduling train journeys and determining the level of demand for rolling stock depending on the season and the annual rise in passenger numbers, increasing the effectiveness of management of rail transport.

Influence of world stock markets on the development of the stock market in Ukraine

Viewing the development of the stock market in Ukraine, the economy, which world financial organizations characterize as small and open, is largely determined by the trends formed by the global stock markets and leading stock exchanges. Therefore, the study aims to analyze Ukraine’s stock market, the world stock market, stock markets in the regions, and to assess their mutual influence. The study uses the data of the World Federation of Exchanges and National Securities and Stock Market Commission (Ukraine) from 2015 to 2020. Stock market performance forecasts are built using triple exponential smoothing. Based on pairwise correlation coefficients, the existence of a significant dependence in the development of the world stock market on the development of the American stock market was determined. Regarding the Ukrainian stock exchanges, only SE “PFTS” demonstrated its dependence on the US stock market. The results of the regression model based on an exponentially smoothed series of trading volumes in all markets showed that variations in the volume of trading on the world stock market are due to the situation on the US stock markets. Trading volume dynamics on Ukrainian stock exchanges such as SE “PFTS” and SE “Perspektiva” is almost 50% determined by the development of stock markets in the American region. Although Ukraine is geographically located in Europe, the results show a lack of significant links and the impacts of stock markets in this region on the major Ukrainian stock exchanges and the stock market as a whole.

Performance analysis of a R134a/CO2 cascade heat pump in severe cold regions of China

To achieve efficient and reliable heating in severe cold regions below −20 °C, this paper investigates a R134a/CO2 cascade air source heat pump (ASHP) system. Firstly, a simulation model is constructed and used for energy performance analysis. Then experiments are carried out for model verification and performance investigation. Finally, its performance is evaluated in detail with the meteorological data of Harbin under different working conditions for energy performance forecast. Simulation results show that the heating coefficient of performance (COP) increases with higher ambient temperature and lower water supply temperature. At the same time, the modeling errors on coefficient of performance (COP) are all within ±0.2%. The experimental results show that the proposed R134a/CO2 ASHP can supply water above 50 °C with high COP up to 3.07 and 1.60 when the ambient air temperature is −5 °C and −45 °C, respectively. Furthermore, under four working conditions, the heating seasonal performance factors (HSPF) are all above 2.30 and the best is 2.39 for the −25 °C nominal operating temperature and the typical working condition of civil buildings (15 °C). Both the experimental data and the simulation results show that the proposed R134a/CO2 cascade air source heat pump system can provide efficient and reliable heating in severe cold regions.

A production-based model for a fractured well in unconventional reservoirs

Due to the depletion of conventional reservoirs and the high demand of energy, unconventional reservoirs will be relied on to supply the world's energy for the foreseeable future. Unfortunately, because of their complex storage and flow mechanisms, modelling and analysis of unconventional reservoirs have been very challenging. Although semi-analytical and numerical models have been proposed, these models rely on somewhat simplifying assumptions and require several input parameters. In this paper, a production-based model is proposed to analyze and predict a fractured-well performance in unconventional reservoirs (specifically, tight and shale reservoirs). The model assumes a power law with a stretched exponential cut-off. While the power-law term governs the transient-state period, the stretched exponential term, which is a superposition of exponential decays, governs the boundary-dominated flow period. As a result, the model is capable of matching both the transient state and boundary-dominated flow portions of the data. The model has been validated with a numerical data and applied to several field data. Results of the model have been compared with other existing models. The findings show that the proposed model yields relatively good estimates, and matches both the transient and boundary-dominated flow periods. The proposed model may contribute to the ongoing efforts to improve the analysis and forecasting of fractured-well performance in unconventional reservoirs.

Dear Computer on My Desk, Which Candidate Fits Best? An Assessment of Candidates' Perception of Assessment Quality When Using AI in Personnel Selection.

Recently, with the increase in technological capabilities and the need to reduce bias in candidate selection processes, artificial intelligence (AI)-based selection procedures have been on the rise. However, the literature indicates that candidate reactions to a selection process need to be considered by organizations that compete for employees. In this study, we investigate reactions to AI-based selection procedures in a three-dimensional vignette study among young adults in Germany. By investigating the effects of the dimensions of AI complexity, intangibility, and reliability on the perceived quality of assessment of potential candidates, we found that AI complexity and intangibility impact the perceived quality of assessment negatively when the candidates’ knowledge, strengths, and weaknesses should be assessed. We also found interactive relationships of all three dimensions for the assessment of motivation. In sum, results indicate that candidates are skeptical toward the assessment quality of AI-intense selection processes, especially if these assess complex assessment criteria such as personality or a job performance forecast. Hence, organizations need to be careful when implementing AI-based selection procedures. HR implications are made on the basis of these results to cope with negative candidate perceptions.

Condition monitoring and performance forecasting of wind turbines based on denoising autoencoder and novel convolutional neural networks

With the proportion of wind power in the grid increasing, the monitoring and maintenance of wind turbines is becoming more and more important for the reliability of the grid. In this study, a data-driven modelling framework based on deep convolutional neural networks is constructed for wind turbines condition monitoring (CM) and performance forecasting (PF). For CM, a robust denoising autoencoder (DAE) model is introduced to output the reconstruction error (RE) of raw signals. The RE is processed to a state indicator by exponentially weighted moving average (EWMA) and monitored on a control chart. For PF, two multi-steps ahead forecasting models are constructed for the forecasting of generator bearing and transformer temperature. To prevent overfitting caused by abundant features, the marginal effect analysis based on random forests is implemented to measure the importance of features. Besides, novel residual attention module (RAM) and training strategies are used improve their representation power of DAE and PF models. Experiments on a real wind turbine dataset prove the effectiveness of the proposed models and methods.

Estimation of Heat Loss Coefficient and Thermal Demands of In-Use Building by Capturing Thermal Inertia Using LSTM Neural Networks

Accurate forecasting of a building thermal performance can help to optimize its energy consumption. In addition, obtaining the Heat Loss Coefficient (HLC) allows characterizing the thermal envelope of the building under conditions of use. The aim of this work is to study the thermal inertia of a building developing a new methodology based on Long Short-Term Memory (LSTM) neural networks. This approach was applied to the Rectorate building of the University of Basque Country (UPV/EHU), located in the north of Spain. A comparison of different time-lags selected to catch the thermal inertia has been carried out using the CV(RMSE) and the MBE errors, as advised by ASHRAE. The main contribution of this work lies in the analysis of thermal inertia detection and its influence on the thermal behavior of the building, obtaining a model capable of predicting the thermal demand with an error between 12 and 21%. Moreover, the viability of LSTM neural networks to estimate the HLC of an in-use building with an error below 4% was demonstrated.

Designing a methodological framework for modeling and performance forecasting of self‐adaptive cloud systems

AbstractAutonomic computing got an increasing attention during past years in the context of Cloud computing in order to optimize the Cloud systems performance and increase their cost‐effectiveness. In that direction, a broad range of various self‐adaptability strategies have been proposed to overcome uncertainties related to Cloud systems like workload variations, hardware failures and malicious attacks. A special attention in the literature has been paid to resource allocation for cost and performance optimization, and self‐healing for better faut‐tolerance. However, while a lot of progress has been done on these topics, there is still a lack of a standardized methodology, easily reproducible and allowing validation and comparison of a self‐adaptability strategies. This article proposes a methodology allowing to forecast the autonomic Cloud systems performances. The idea is to compare different self‐adaptability approaches at the design stage, and identify the most adequate and optimized configuration. This methodology is based on a new architecture description language and a stochastic Petri nets‐based modeling and simulation approach. We illustrate our methodology in this article through a running example and a set of experiments.

An Advanced Decision Making Framework via Joint Utilization of Context-Dependent Data Envelopment Analysis and Sentimental Messages

Compared to widely examined topics in the related literature, such as financial crises/difficulties in accurate prediction, studies on corporate performance forecasting are quite scarce. To fill the research gap, this study introduces an advanced decision making framework that incorporates context-dependent data envelopment analysis (CD-DEA), fuzzy robust principal component analysis (FRPCA), latent Dirichlet allocation (LDA), and stochastic gradient twin support vector machine (SGTSVM) for corporate performance forecasting. Ratio analysis with the merits of easy-to-use and intuitiveness plays an essential role in performance analysis, but it typically has one input variable and one output variable, which is unable to appropriately depict the inherent status of a corporate’s operations. To combat this, we consider CD-DEA as it can handle multiple input and multiple output variables simultaneously and yields an attainable target to analyze decision making units (DMUs) when the data present great variations. To strengthen the discriminant ability of CD-DEA, we also conduct FRPCA, and because numerical messages based on historical principles normally cannot transmit future corporate messages, we execute LDA to decompose the accounting narratives into many topics and preserve those topics that are relevant to corporate operations. Sequentially, the process matches the preserved topics with a sentimental dictionary to exploit the hidden sentiments in each topic. The analyzed data are then fed into SGTSVM to construct the forecasting model. The result herein reveals that the introduced decision making framework is a promising alternative for performance forecasting.