Cost Prediction Model Research Articles

Hybrid wavelet/Elman NN model for short term cost prediction utilizing developed deer hunting optimizer

The cost signal of electricity in the competitive electrical energy marketplaces is of special importance for all planning and operation activities. Also, the price of electricity has an uncertain nature and various factors affect it in the short and long term. Factors active in the electricity market need to accurately and effectively forecast the electricity price signal to manage risk in the market. For estimating future electricity prices, this research suggests a combined procedure on the basis of Elman neural network model and the wavelet transform. The proposed Elman neural network/wavelet transform forecasted the next hour's power price based on the past 24 h' pricing. This research uses an optimized Elman neural network using a developed deer hunting optimizer and the total model is named Elman neural network/developed deer hunting optimization-wavelet transform. In this paper, Data of Zone Preliminary Billing is used for establishing the training of model and forecasting of performance. The method is then compared with some other published works and the outcomes demonstrate the offered approach superiority toward those for the electrical cost predicting.

Development and Validation of the US Diabetes, Obesity, Cardiovascular Disease Microsimulation (DOC-M) Model: Health Disparity and Economic Impact Model.

Few simulation models have incorporated the interplay of diabetes, obesity, and cardiovascular disease (CVD); their upstream lifestyle and biological risk factors; and their downstream effects on health disparities and economic consequences. We developed and validated a US Diabetes, Obesity, Cardiovascular Disease Microsimulation (DOC-M) model that incorporates demographic, clinical, and lifestyle risk factors to jointly predict overall and racial-ethnic groups-specific obesity, diabetes, CVD, and cause-specific mortality for the US adult population aged 40 to 79 y at baseline. An individualized health care cost prediction model was further developed and integrated. This model incorporates nationally representative data on baseline demographics, lifestyle, health, and cause-specific mortality; dynamic changes in modifiable risk factors over time; and parameter uncertainty using probabilistic distributions. Validation analyses included assessment of 1) population-level risk calibration and 2) individual-level risk discrimination. To illustrate the application of the DOC-M model, we evaluated the long-term cost-effectiveness of a national produce prescription program. Comparing the 15-y model-predicted population risk of primary outcomes among the 2001-2002 National Health and Nutrition Examination Survey (NHANES) cohort with the observed prevalence from age-matched cross-sectional 2003-2016 NHANES cohorts, calibration performance was strong based on observed-to-expected ratio and calibration plot analysis. In most cases, Brier scores fell below 0.0004, indicating a low overall prediction error. Using the Multi-Ethnic Study of Atherosclerosis cohorts, the c-statistics for assessing individual-level risk discrimination were 0.85 to 0.88 for diabetes, 0.93 to 0.95 for obesity, 0.74 to 0.76 for CVD history, and 0.78 to 0.81 for all-cause mortality, both overall and in three racial-ethnic groups. Open-source code for the model was posted at https://github.com/food-price/DOC-M-Model-Development-and-Validation. The validated DOC-M model can be used to examine health, equity, and the economic impact of health policies and interventions on behavioral and clinical risk factors for obesity, diabetes, and CVD. We developed a novel microsimula'tion model for obesity, diabetes, and CVD, which intersect together and - critically for prevention and treatment interventions - share common lifestyle, biologic, and demographic risk factors.Validation analyses, including assessment of (1) population-level risk calibration and (2) individual-level risk discrimination, showed strong performance across the overall population and three major racial-ethnic groups for 6 outcomes (obesity, diabetes, CVD, and all-cause mortality, CVD- and DM-cause mortality)This paper provides a thorough explanation and documentation of the development and validation process of a novel microsimulation model, along with the open-source code (https://github.com/food-price/ DOCM_validation) for public use, to serve as a guide for future simulation model assessments, validation, and implementation.

Index hospital cost of adverse events following thoracic surgery: a systematic review of economic literature

ObjectivesAdverse events (AEs) following thoracic surgery place considerable strain on healthcare systems. A rigorous evaluation of the economic impact of thoracic surgical AEs remains lacking and is required to understand...

Prediction and Analysis of House Price Through Machine Learning Approach

In recent years, picture detection, spam restructuring, natural speech command, product recommendation, and medical diagnosis have all benefited greatly from machine learning. The current machine learning algorithm aids in improving medical advancements, public safety, and security alerts. In the current work, we talk about the machine learning algorithm-generated forecast of future home prices. We compare and investigate numerous prediction methods for the selection of prediction methods. We employ lasso regression as our model because to its flexible and probabilistic model selection process We construct a housing cost prediction model in the considering such machine learning algorithm models as XG Boost, lasso regression, and neural systems on look at their order precision execution. At that point, we urge a real estate agent or home seller to employ a housing cost estimates model to assist them with better information based on property valuation. In regards to precision those tests show that the lasso regression algorithm constantly beats alternative models when utilized to predict costs associated with housing.

Life Cycle Cost Method for Safe and Effective Infrastructure Asset Management

Existing maintenance management systems implement periodic inspections and diagnoses and perform maintenance to restore damaged facilities, making it difficult to establish a long-term and analytical budget plan. The Framework Act on Sustainable Infrastructure Management necessitates specific implementation plans for new implementation items. This study proposes a detailed method for estimating infrastructure management cost to overcome the limitations of the post-response maintenance system and establish a management plan for the Framework Act on Sustainable Infrastructure Management, considering the performance and cost effects in terms of the life cycle. The method was classified into the following stages: analysis of the performance degradation timing by deriving the performance degradation curve, analysis of proper construction methods by performance grade to establish a cost model for each member grade, representative life assessment of the establishment to determine the end-of-life of members, and analysis of optimal action timing for establishing short/mid- to long-term repair and reinforcement plans. The proposed method was applied to a water reservoir (99 reservoirs in Seoul, Korea). The performance degradation and cost prediction models for the target establishment were analyzed. The proposed method can be applied to the maintenance decision making of the management agency and is significant for efficient infrastructure maintenance.

HOUSE PRICE PREDICTION USING MACHINE LEARNING

Machine Learning (ML) has profoundly impacted various domains, including speech recognition, healthcare, and automotive safety. Acknowledging its pervasive influence, our project aims to harness ML's capabilities for housing price prediction. In the volatile real estate market, prospective buyers strive to make informed decisions within budget constraints, often hindered by the absence of reliable future market trend forecasts. Our project's primary goal is to provide accurate house price predictions, mitigating potential financial losses. To achieve this, we are developing a housing cost prediction model employing ML algorithms such as Linear Regression, Decision Tree Regression, KMeans Regression, and Random Forest Regression. This model empowers individuals to invest in real estate without intermediaries. Our research highlights Random Forest Regression as the most accurate model, offering a promising avenue for confident real estate investment.

Autonomous topology planning for distribution network expansion: A learning-based decoupled optimization method

The distribution of urban electricity load is highly uneven in both space and time, posing significant challenges to the problem of distribution network expansion planning (DNEP). An autonomous topology planning method for distribution network expansion is proposed to address that, driven by a learning-based decoupled optimization approach. The planning problem is decoupled from the impact of distribution network operation and used as reinforcement cost prediction for autonomous planning after the generation of topology schemes. First, a reinforcement model of the original distribution network is built that considers the actual distance calculation method of load nodes. Then an effective method for generating historical operation samples is designed, followed by the development of a learning-based reinforcement cost prediction model for the original distribution network based on operation state learning using XGBoost, which enables the prediction of said costs under different scenarios. Utilizing a distribution network topology generating method driven by the adjustable weight minimum spanning tree (AWMST) algorithm, an autonomous topology expansion planning strategy generation method employing the learning-based decoupled optimization approach is established. The feasibility and superior analysis of the proposed method is verified and analyzed on an actual urban distribution network case with simulation scenarios. The results show that the proposed method is faster and more flexible to deal with the distribution network planning problem brought about by rapid load growth in urban distribution networks.

A hybrid machine learning approach for early cost estimation of pile foundations

Purpose This study aims at proposing a hybrid model for early cost prediction of a construction project. Early cost prediction for a construction project is the basic approach to procure a project within a predefined budget. However, most of the projects routinely face the impact of cost overruns. Furthermore, conventional and manual cost computing techniques are hectic, time-consuming and error-prone. To deal with such challenges, soft computing techniques such as artificial neural networks (ANNs), fuzzy logic and genetic algorithms are applied in construction management. Each technique has its own constraints not only in terms of efficiency but also in terms of feasibility, practicability, reliability and environmental impacts. However, appropriate combination of the techniques improves the model owing to their inherent nature. Design/methodology/approach This paper proposes a hybrid model by combining machine learning (ML) techniques with ANN to accurately predict the cost of pile foundations. The parameters contributing toward the cost of pile foundations were collected from five different projects in India. Out of 180 collected data entries, 176 entries were finally used after data cleaning. About 70% of the final data were used for building the model and the remaining 30% were used for validation. Findings The proposed model is capable of predicting the pile foundation costs with an accuracy of 97.42%. Originality/value Although various cost estimation techniques are available, appropriate use and combination of various ML techniques aid in improving the prediction accuracy. The proposed model will be a value addition to cost estimation of pile foundations.

Modelling rework cost for TETFund building construction projects in Nigeria

Accurate predictions of rework cost enable contractors to evaluate how successful their projects are likely to be, thus, improve their operations and productivity. Studies have developed project scope-based and non-scopebased rework cost predictive models. However, researchers have criticized the accuracy of these models because of their weaknesses in giving individualistic treatments to project scope-based and non- scope-based rework cost influencing factors. This study therefore aimed at developing rework cost predictive models that combine the synergistic effects of both project scope- based and non-scope-based rework cost factors. 440 sample size was drawn from 2,638 population of Tertiary Education Trust Fund (TETfund) building projects constructed between 2009 to 2015. Data of 287 (65%) of 440 (sample size) of completed TETfund building projects on project scope factors as well as the extent of influence of the non-scope factors on cost of rework in TETfund building projects was collected. Principal Component Analysis (PCA) and Multiple Linear Regression (MLR) were used in developing an integrated rework cost predictive model. More accuracy was achieved in the integrated model through the incorporation of impact of project scope and non-scope influencing factors into rework cost prediction model, this increased the variability in cost of rework to R2 = 0.759 (75.9%), while errors in rework cost predictions reduced by as much as 9.75%.The model serve as useful tool used to enhance accuracy of rework cost predictions for TETfund building contractors, thus reduce cost of rework for building construction projects.

Assessing Carbon Abatement Costs Considering Forest Carbon Sequestration and Carbon Offset Mechanism: Evidence from Taiwan

Abstract Based on the Greenhouse Gas Reduction and Management Act passed in 2015 and the carbon neutral target in 2050, Taiwan will most likely follow international trends by imposing carbon taxes and establishing carbon offset markets. The positive and negative effects of carbon taxes and carbon offset markets on the economy and the environment merit further investigation. Accordingly, this study adopted a carbon emission reduction (CER) cost prediction model to assess the carbon abatement costs under three scenarios: (1) a carbon offset market exists, and forest carbon sequestration can be used as carbon offsets; (2) a carbon offset market exists, but forest carbon sequestration cannot be used as carbon offsets; and (3) a carbon offset market does not exist. Forests in Taipei (with low carbon emissions) and Kaohsiung (with high carbon emissions) were selected as research sites to explore the benefits of carbon emissions trading and forest carbon sequestration. The results show that CER costs are the lowest in scenario 1 and are the highest in scenario 3. The CER costs of Kaohsiung are higher than those of Taipei. The higher the carbon price, the greater the difference in CER costs between the two cities. Study Implications: The objective of this study was to identify the optimal policy for Taiwan to effectively slow climate change. This study showed that the opening of carbon offset markets and the use of forest carbon sequestration as carbon offsets may prompt regions to increase their forest stock to lower their emission reduction costs. However, achieving 2050 carbon neutral target by solely using forest carbon sequestration is not sufficient in Taiwan.

Multidimensional analysis of engineering cost database based on descriptive data mining

A vital area of study is cost estimation in building engineering. Traditional models' poor capacity to appropriately capture the changing trend of construction engineering costs leads to low precision in cost forecast for construction engineering. The result is the recommendation of a descriptive data mining-based cost prediction model for construction engineering. The engineering cost database is initially constructed using a number of distinctive indices, and the aberrant data are discovered and filtered using the K-means clustering method. Second, the mathematical model is created, and the LSSVM is used to solve it. The model is then optimized using the PSO method. According to the experimental findings, this model's prediction accuracy is good and its average root-mean-square error (RMSE) for 100 samples is 2.27-e. The estimated value of individual cost in this model is the most accurate when compared to other models, and the predicted difference is just 30 Yuan/m2. Because it can estimate engineering costs with greater accuracy and stability, this finding has important implications for engineering investment choices.

Prediction of the maintenance cost of general aviation aircraft based on engineering method

Purpose General aviation aircraft has a wide range of applications, and effective cost management is one of the hot spots in the research of general aviation manufacturers. The purpose of this paper is to build a flexible engineering method to predict maintenance cost of general aviation aircraft. Design/methodology/approach To establish a reasonable general aviation aircraft maintenance cost prediction model, it is necessary to analyze the influencing factors and extract the main components of maintenance cost. The maintenance cost is divided by engineering method, and the estimation model of each component cost is established. Then, the general aviation aircraft maintenance cost model is obtained. The results show that the relative error of this method is between 13% and 20%, which has a good estimation accuracy and can be effectively used to estimate the maintenance cost of general aviation aircraft. Findings The maintenance cost plays an important role in the life cycle cost of general aviation aircraft. Accurate cost prediction method is of great significance to the optimal design of general aviation aircraft. However, there are few prediction models suitable for maintenance cost, the proposed approach is meaningful and quite desirable. Originality/value To some extent, this method overcomes the shortage of the work on maintenance cost prediction for general aviation aircraft. The model established in this paper has certain generality, which can provide some reference for general aviation aircraft design and operation enterprises.

Prediction Models for Individual-Level Healthcare Costs Associated with Cardiovascular Events in the UK.

The aim of this study was to develop prediction models for the individual-level impacts of cardiovascular events on UK healthcare costs. In the UK Biobank, people 40-70 years old, recruited in 2006-2010, were followed in linked primary (N=192,983 individuals) and hospital care (N=501,807 individuals) datasets. Regression models of annual primary and annualhospital carecosts(2020 UK£) associated with individual characteristics and experiences of myocardial infarction (MI), stroke, coronary revascularization, incident diabetes mellitus and cancer, and vascular and nonvascular death are reported. For both people without and with previous cardiovascular disease (CVD), primary care costs were modelled using one-part generalised linear models (GLMs) with identity link and Poisson distribution, and hospital costs with two-part models (part 1: logistic regression models theprobability of incurring costs; part 2: GLM with identity link and Poisson distribution models thecosts conditional on incurring any). In people without previous CVD, mean annual primary and hospital care costs were £360 and £514, respectively. The excess primary care costs were £190 and £360 following MI and stroke, respectively, whereas excess hospital costs decreased from £4340 and £5590, respectively, in the year of these events, to £190 and £410 two years later. People with previous CVD had more than twice higher annual costs, and incurred higher excess costs for cardiovascular events. Other characteristics associated with higher costs included older age, female sex, south Asian ethnicity, higher socioeconomic deprivation, smoking, lower level of physical activities, unhealthy body mass index, and comorbidities. These individual-level healthcare cost prediction models could inform assessments of the value of health technologies and policies to reduce cardiovascular and other disease risks and healthcare costs. An accompanying Excel calculator is available to facilitate the use of the models.

Cost and Material Quantities Prediction Models for the Construction of Underground Metro Stations

This paper addresses the gap in the scientific literature regarding construction cost estimates for the construction of underground metro stations. It provides preliminary cost estimation models using linear regression for use by the Greek underground metro public transport authority for planning future extensions to the Athens and Thessaloniki networks. At the same time, it contributes to the body of knowledge by proposing material quantity prediction models and presents a two-stage preliminary cost estimation model for the construction of civil engineering works of underground metro stations. Stage one uses the construction cost budgets of six metro stations in Greece to develop a multilinear regression equation for the prediction of the overall cost for construction of civil engineering works; stage two provides estimates of material quantities using linear regression, key quantity ratios, and artificial neural networks. The data analyzed are from the prior measurements of quantities for the construction of the Chaidari to Piraeus extension of the Athens Metro Line 3. After comparing the actual values of costs and quantities with the corresponding predictions, acceptable discrepancies are observed. All models provide estimates within ±25% discrepancies, which are acceptable at the conceptual planning phase in order to initiate project funding quests.

Maintenance cost prediction for the vehicle based on maintenance data

With the fierce competition in the automobile market, the focus of competition in the automobile industry had gradually turned to the automobile extended warranty service. The prediction of maintenance cost was a very important premise for the formulation of automobile extended warranty service. Combining the failure frequency data of vehicles in multiple sales batches, the mixed Weibull model was used to fit the failure process, and the single vehicle failure rate prediction model was obtained; At the same time, combined with the maintenance cost data of the same batch of vehicles, the prediction model of single vehicle maintenance cost was obtained by iterative solution. Using the existing maintenance data, the maintenance cost prediction model based on user group had been verified. The results showed that the model was real, effective and had strong engineering application value.

The economic analysis for hydrogen production cost towards electrolyzer technologies: Current and future competitiveness

Hydrogen production by electrolysis technology spurs as extensive investigation toward new clear energy acquisition. The mainstream hydrogen production electrolyzers, including alkaline electrolyzer (ALK), anion exchange membrane electrolyzer (AEM), and proton exchange membrane electrolyzer (PEM), are traced to compare their current and future hydrogen production cost regarding technology development. Technologies' characteristics are originally described as the polarization curve parameters such as current density, overpotential, and polarization curve slope. The feature of crucial materials such as catalysts and membranes are also taken into consideration. Then, a bottom-up hydrogen production cost prediction model stemming from technical factors is established with a combination of manufacturing and operating considerations. According to model predictions, the cost of hydrogen production of ALK will be 23.85% and 51.59% lower than AEM and PEM technologies in the short term. However, under technological advancement or breakthrough, the hydrogen production cost of AEM and PEM is expected to be 24% and 56.5% lower in the medium-term and long-term, respectively. The lifetime of the electrolyzers is significantly vital to affect the cost of hydrogen production. The cost reduction space brought about by various technical factors is also explored for the blueprint planning of the hydrogen economy.

Prediction of Shipping Cost on Freight Brokerage Platform Using Machine Learning

Not having an exact cost standard can present a problem for setting the shipping costs on a freight brokerage platform. Transport brokers who use their high market position to charge excessive commissions can also make it difficult to set rates. In addition, due to the absence of a quantified fare policy, fares are undervalued relative to the labor input. Therefore, vehicle owners are working for less pay than their efforts. This study derives the main variables that influence the setting of the shipping costs and presents the recommended shipping cost given by a price prediction model using machine learning methods. The cost prediction model was built using four algorithms: multiple linear regression, deep neural network, XGBoost regression, and LightGBM regression. R-squared was used as the performance evaluation index. In view of the results of this study, LightGBM was chosen as the model with the greatest explanatory power and the fastest processing. Furthermore, the range of the predicted shipping costs was determined considering realistic usage patterns. The confidence interval was used as the method of calculation for the range of the predicted shipping costs, and, for this purpose, the dataset was classified using the K-fold cross-validation method. This paper could be used to set the shipping costs on freight brokerage platforms and to improve utilization rates.

Building Engineering Cost Prediction Model Based on TSNE and Improved Grey Correlation Algorithm

With the development of digitalization in the construction industry, the complexity of construction projects is increasing. In response to the widely valued cost and quality management of construction projects, improved algorithms based on machine vision learning can effectively cope with the increasingly complex cost prediction of construction projects. In the entire model design, how to complete the selection and processing of key indicators for construction projects is currently a key issue that needs to be urgently solved. This article studied the use of TSNE (T-Distributed Stochastic Neighbor Embedding) and improved grey correlation algorithm to design a cost prediction model, aiming to improve the prediction accuracy of construction project costs and reduce system prediction time. This article analyzed the cost index system and simulation experiments of construction projects, applied machine learning based TSNE and improved grey correlation algorithm, combined index dimensionality reduction processing and construction project unit test set samples, and discussed the following conclusions based on the data results: In the context of intelligent large-scale infrastructure, the construction project cost prediction model using TSNE and improved grey correlation algorithm had higher prediction accuracy than traditional prediction methods in all construction project samples, with an average improvement of 5.1%. At the same time, in terms of model prediction efficiency, the improved algorithm was better than traditional prediction methods, with an average improvement of 12.75%. This indicated that the construction project cost prediction model based on TSNE and improved grey correlation algorithm has a relatively good prediction effect overall.

Research on Construction Project Cost Prediction Model Based on Recurrent Neural Network

At present, the conventional construction project cost prediction method mainly constructs the cost prediction model by quantifying the engineering information, which leads to poor prediction effect due to the lack of construction of cost prediction index system. In this regard, the research of construction cost prediction model based on recurrent neural network is proposed. By classifying and integrating the construction project cost concepts, constructing the prediction index system, combining with the recurrent neural network algorithm, constructing the excitation function and calculating the initialization threshold, and finally constructing the prediction model. In the experiment, the proposed method is verified for the prediction accuracy. After the experiments, it can be proved that when the proposed model is used to predict the engineering cost, the root mean square error of the model output is small and has a more ideal prediction accuracy.

Research on Civil Engineering Cost Prediction Based on Decision Tree Algorithm

Civil engineering includes not only all engineering construction on the ground, but also the maintenance, exploration and design of equipment and materials used in the whole construction process. During the construction of civil engineering, there is no scientific and reasonable supervision system for the supervision of construction, which greatly affects the quality of engineering and the control and management of engineering cost. In this paper, through the application of DT (Decision tree) algorithm, the research of civil engineering cost prediction is carried out. Aiming at the application of the algorithm in civil engineering cost management, this paper tries to improve the C4.5 algorithm. DT civil engineering life-cycle cost analysis and prediction model is trained by training set, and the optimal result of civil engineering life-cycle cost analysis is obtained by inputting sample data for model prediction. The application results show that the above algorithm has lower computational complexity and higher prediction efficiency in civil engineering cost prediction, which can better meet the needs of actual civil engineering cost analysis and prediction.