scholarly journals Battery cost forecasting: a review of methods and results with an outlook to 2050

2021 ◽  
Author(s):  
Lukas Mauler ◽  
Fabian Duffner ◽  
Wolfgang G. Zeier ◽  
Jens Leker
Keyword(s):  
Cost Forecasting

This review analyzes 53 publications that forecast battery cost and provides transparency on methodological and technological details.

Cost Forecasting of Power Engineering based on RF and SVM optimized by WPA

2021 ◽  
Author(s):  
Xiaojun Gao ◽  
Ping Zhou ◽  
Kuiyun Zhao ◽  
Jie Jiao ◽  
Yun Wang ◽  
...  
Keyword(s):  
Power Engineering ◽  
Cost Forecasting

scholarly journals THE OPTIMAL PRINCIPLE OF BELLMAN IN THE PROBLEM OF OPTIMAL MEANS DISTRIBUTION BETWEEN ENTERPRISES FOR THE EXPANSION OF PRODUCTION

2019 ◽  
pp. 132-138 ◽  
Author(s):  
A. Tarasenko ◽  
I. Egorova
Keyword(s):  
Dynamic Programming ◽  
Nonlinear Programming ◽  
Optimal Solution ◽  
Optimal Distribution ◽  
Optimality Principle ◽  
Numerical Example ◽  
Maximum Profit ◽  
Cost Forecasting ◽  
Method Of Dynamic Programming ◽  
Minimum Costs

The method of dynamic programming has been considered, which is used in solving multiple problems in economics, on the example of using Bellman’s optimality principle for solving nonlinear programming problems. On a specific numerical example, the features of the solution have been shown in detail with all the calculations. The problem of optimal distribution of funds among enterprises for the expansion of production has been formulated, which would give the maximum total increase in output. The solution of the task has been presented in the case, when the number of enterprises is 3. It has been shown, that the Bellman optimality principle allows you solve applied problems of cost forecasting with obtaining the optimal solution-maximum profit at minimum costs.

scholarly journals Conventional and Current way of Interpretation and Cost Forecasting

2020 ◽  
Vol 1 (6) ◽  
pp. 30-33
Author(s):  
Shahin ripon Nazmul ◽  
Riyaaz Sanjoy
Keyword(s):  
Regression Analysis ◽  
Production Process ◽  
Cross Section ◽  
Series Data ◽  
Cross Section Data ◽  
Short Term ◽  
Section Data ◽  
Production Factors ◽  
Cost Forecasting

This study discuses Short-term cost interpretation, regression analysis with time-series data, long term cost interpretation, Regression analysis using cross-section data, cost forecasting and Changes in the productivity of production factors. Short-term cost interpretation lead to short-term decisions, the concept of incramental costs has a very important role which includes variable costs and changes in fixed costs.  Long term cost interpretation to analyze the production function of several different firms, long-run cost estimates can be used. Based on these conditions, the estimation of long-term costs uses cross-section data. Forecasting costs for various levels of output in the coming period requires an assessment of changes in the efficiency of the production process physically, plus changes in the prices of production factors used in the production process.

Some Linear Fuzzy Collaborative Forecasting Models for Semiconductor Unit Cost Forecasting

2013 ◽  
Vol 3 (1) ◽  
pp. 25-39
Author(s):  
Toly Chen
Keyword(s):  
Unit Cost ◽  
Product Type ◽  
Important Task ◽  
Experimental Results ◽  
Distributed Environment ◽  
Forecasting Models ◽  
Cost Forecasting

Forecasting the unit cost of every product type in a factory is an important task to the factory. However, it is not easy to deal with the uncertainty in the unit cost. Fuzzy collaborative forecasting is very effective in handling the uncertainty in the distributed environment. This paper presents some fuzzy collaborative forecasting models to predict the unit cost of a semiconductor product. According to the experimental results, the effectiveness of forecasting the unit cost was considerably improved through the objects’ collaboration.

Cost Forecasting and Price Policy

1949 ◽  
Vol 13 (3) ◽  
pp. 279-288
Author(s):  
Joel Dean
Keyword(s):  
Price Policy ◽  
Cost Forecasting

Construction Project Costs Management Research

2014 ◽  
Vol 1079-1080 ◽  
pp. 1115-1118
Author(s):  
Lu Liu
Keyword(s):  
Cost Control ◽  
Construction Project ◽  
Project Cost ◽  
Control Goal ◽  
Project Costs ◽  
Construction Project Cost ◽  
Cost Forecasting ◽  
The Cost ◽  
Cost Plan ◽  
And Control

Themain content of the construction project cost management including costforecast, cost plan, cost control. Do cost forecast, cost control goal set,must be ahead of the labor, material, cost forecasting, construction scheme ofchange of the cost forecast and the prediction of auxiliary construction cost.Cost control should follow the principle of conservation and comprehensivecontrol, there are some effective ways to realize the cost control, forexample, take organizational measures to control the project cost, take technicalmeasures to control the project cost, to take economic measures to control theproject cost, strengthen quality management and control of rework rate, etc.

scholarly journals Comparing univariate techniques for tender price index forecasting: Box-Jenkins and neural network model

2017 ◽  
Vol 17 (3) ◽  
pp. 109-123 ◽  
Author(s):  
Olalekan Oshodi ◽  
Obuks Augustine Ejohwomu ◽  
Ibukun Oluwadara Famakin ◽  
Paulo Cortez
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Price Index ◽  
Poor Performance ◽  
Construction Cost ◽  
Multivariate Models ◽  
The Neural Network ◽  
Modelling Techniques ◽  
Cost Forecasting

The poor performance of projects is a recurring event in the construction sector. Information gleaned from literature shows that uncertainty in project cost is one of the significant causes of this problem. Reliable forecast of construction cost is useful in mitigating the adverse effect of its fluctuation, however the availability of data for the development of multivariate models for construction cost forecasting remains a challenge. The study seeks to investigate the reliability of using univariate models for tender price index forecasting. Box-Jenkins and neural network are the modelling techniques applied in this study. The results show that the neural network model outperforms the Box-Jenkins model, in terms of accuracy. In addition, the neural network model provides a reliable forecast of tender price index over a period of 12 quarters ahead. The limitations of using the univariate models are elaborated. The developed neural network model can be used by stakeholders as a tool for predicting the movements in tender price index. In addition, the univariate models developed in the present study are particularly useful in countries where limited data reduces the possibility of applying multivariate models.

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