Demand Forecast Model Research Articles

Estimation of interregional rail passengers’ valuations for their desired departure times

Valuations for various attributes of the transport supply are key parameters in travel demand forecast models and cost-benefit analyses. In the case of interregional rail travel, such attributes are mostly travel time, departure/arrival time, and comfort/service levels. Although the valuations for travel time savings and comfort levels are well documented, literature concerning how much passengers are willing to pay to obtain the departure/arrival time that best suits their needs remains scarce. We present in this paper a new study that estimates passenger valuations for reduction of departure time displacement (also called schedule delay) through common adaptation of the scheduling model. Our goal is twofold: first, better understand how travel scheduling is influenced by socioeconomic backgrounds and trip characteristics; second, provide detailed figures that can be used to improve travel demand forecasts and cost-benefit analyses. To achieve this, we conducted a stated preference survey on several Swedish rail routes and determined the valuations for departure time scheduling as willingness to pay and time multipliers. The figures obtained show that departure time flexibility greatly depends on trip characteristics and travellers’ socio-economic background. In addition, the comparison of our figures with previous literature highlights the need to establish a standardised method to measure and use these valuations. Finally, we succeeded in providing valuations that can be used with care as approximations in demand modelling and cost-benefit analyses in the context of interregional rail travel.

Integrated RAGA-PP water demand forecast model (case study: Shaanxi Province, China)

Abstract The demand for water resources in Shaanxi Province increases greatly due to the continuous growth of its population and the rapid development of the social economy. Water demand forecasting is a significant issue in the designing, maintaining and operating of a reliable and economical water supply system. An explicit mathematical method was presented in this study, based on the indicators of industrial output value, such as the gross output value of agriculture, forestry, animal husbandry and fishery. The impact of total retail sales and year trends in the domestic or industrial water demands, can accurately forecast the water demand fluctuations for a municipality. Adopt RAGA-PP optimal selection model through a grouping method of data handling for water demand management to test for the case study Shaanxi, China. Results showed that the prediction effect of multivariate logarithmic model accuracy can reach 99.50%, and it is estimated that the demand for water resources in Shaanxi would exceed 10 billion m3 by 2030. The average relative error of the water consumption from 2015 to 2017 is 3.05% for the model of multiple linear and 0.50% for the model of multivariate logarithm model. Our framework can assist in developing sustainable solutions.

Models for forecasting water demand using time series analysis: a case study in Southern Brazil

Abstract Technology has been increasingly applied in search for excellence in water resource management. Tools such as demand-forecasting models provide information for utility companies to make operational, tactical and strategic decisions. Also, the performance of water distribution systems can be improved by anticipating consumption values. This work aimed to develop models to conduct monthly urban water demand forecasts by analyzing time series, and adjusting and testing forecast models by consumption category, which can be applied to any location. Open language R was used, with automatic procedures for selection, adjustment, model quality assessment and forecasts. The case study was conducted in the city of Joinville, with water consumption forecasts for the first semester of 2018. The results showed that the seasonal ARIMA method proved to be more adequate to predict water consumption in four out of five categories, with mean absolute percentage errors varying from 1.19 to 15.74%. In addition, a web application to conduct water consumption forecasts was developed.

Demand Forecasting model based on artificial neural networks for Passenger Transportation Projects

Abstract Whereas passenger demand is one of the main risks in passenger transport infrastructure projects on track, this paper aims to propose a demand forecasting model based on artificial neural networks (ANN) in order to contribute to the project management still in its early planning stages. For this, the design of ex post facto type was used in a descriptive research with quantitative approach, where the research group was composed by subway and train stations in the metropolitan region of São Paulo-Brazil. In total, 12 ANN were proposed architectures with 15 different configurations, totaling 180 training processes, testing and validation. For each architecture has been identified the lowest mean square error percentage obtained; and the best architecture, with a hidden layer, was performed relevance analysis by Garson method, the model 4 input variables: the population; the school enrolment; the number of jobs; and per capita income. With the proposed model, one expects to contribute to the theory by adding to demand forecasting models using a robust methodology and, for managers, serve as a tool in studies of economic and financial viability of these projects, still in its planning phase anticipated as an investment decision-making tool.

Optimal Operation of an Urban Microgrid Using Model Predictive Control Considering Power Quality Improvements

HIGHLIGHTS Optimal Microgrid operation for both grid-connected and island mode. Detailed modelling of the distribution system and the generation devices. Load demand and irradiation forecasting models for Model Predictive Control solution. Power quality improvements concerning planning and operation.

Analysis of Enterprise Human Resources Demand Forecast Model Based on SOM Neural Network.

Human resource planning is the prerequisite of human resource management, and the basic work of human resource planning is to predict human resource demand. Scientific and reasonable human resource demand forecasting results can provide important data support for enterprise human resource planning and strategic decision-making so that human resources management can play a better role in the realization of corporate goals. Because human resource demand is affected by many factors, there is a high degree of nonlinearity and uncertainty between each factor and personnel demand, as well as the incompleteness and inaccuracy of corporate human resource data. In this paper, the self-organizing feature mapping (SOM) artificial neural network prediction model is selected as the prediction model, and the input and output process of sample data is converted into the optimal solution process of the nonlinear function. In the application of the model, the human resource demand prediction index system is used as the input of the SOM neural network and the total number of employees in the enterprise is used as the output so that the problem of nonlinear fitting between human resource demand-influencing factors and human resource demand can be solved. Finally, through the empirical analysis of the enterprise, the model forecasting process is explained and the human resource demand forecast is realized.

A Forecast Model of Tourism Demand Driven by Social Network Data

To improve the forecasting accuracy of tourism demand through forecasting model and data sources, this paper takes the social network data as an entry point, and collects the social network data by the web crawler, then quantifies the data based on the sentiment analysis of the BERT model. This paper uses structured variables such as social network data, weather, holidays, etc. to build a tourism demand forecasting model based on Gradient Boosting Regression Trees. At last, take Huang Shan as example, use actual statistics of passenger terminal and social network data to do an empirical analysis of Huang Shan tourism demand forecasting. Compared with the existing model and introduce ablation study to verify the effectiveness of the considered factors. The result shows that the model based on social network data has improved the forecasting accuracy from the existing ones, ablation study shows social network data helps to improve the accuracy of tourism demand forecasting.

Talent Demand Forecasting Model with Practicability Based on the Theory of ARIMA

The urban development depends on talents, meanwhile, different types of talent demand affects the development direction of a city. The actual talent demand is affected by many factors, and can reflect the economic capacity of a city to a certain extent. Our goal is to predict the potential talent demand of A-City in the next three years. First, we establish a multiple regression model and come to the conclusion that the relationship between talent demand and job demand is lineal and desired profession has a positive effect on talent demand while desired educational background has a negative effect on talent demand. Based on the above analysis results and the theory of ARIMA, we establish a talent demand forecasting model. Finally, we predict the talent demand and three factors of A-City in the next three years.

Energy Demand Forecast in Yunnan Province Based on Seq2Seq Model

Energy demand forecast has an important practical significance for the sustainable development of the national economy, the reasonable allocation of resources, and the construction of modernization goals. This study is based on the analysis of coal, electricity, natural gas, and other energy data in Yunnan Province from 2011 to 2018 and uses long short-term memory, sequence to sequence, deep learning, and ridge regression coupling methods to construct an energy demand forecast model in Yunnan Province. Forecast results show the following. The total energy consumption of Yunnan Province from 2021 to 2025 will continue to increase. Moreover, the coal consumption of Yunnan Province will continue to decline from 2021 to 2025. Furthermore, the electricity consumption of Yunnan will increase by about 8.02% year-on-year from 2021 to 2025. The experiment proves that the forecasting effect of the energy demand forecast model proposed in this study is excellent.

김 수급 전망모형 구축에 관한 연구

김 수급 전망모형 구축에 관한 연구

Analysis of the Recall Demand Pattern of Imported Cars and Application of ARIMA Demand Forecasting Model

This research explores how imported automobile companies can develop their strategies to improve the outcome of their recalls. For this, the researchers analyzed patterns of recall demand, classified recall types based on the demand patterns and examined response strategies, considering plans on how to procure parts and induce customers to visit workshops, recall execution capacity and costs. As a result, recalls are classified into four types: U-type, reverse U-type, L- type and reverse L-type. Also, as determinants of the types, the following factors are further categorized into four types and 12 sub-types of recalls: the height of maximum demand, which indicates the volatility of recall demand; the number of peaks, which are the patterns of demand variations; and the tail length of the demand curve, which indicates the speed of recalls. The classification resulted in the following: L-type, or customer-driven recall, is the most common type of recalls, taking up 25 out of the total 36 cases, followed by five U-type, four reverse L-type, and two reverse U-type cases. Prior studies show that the types of recalls are determined by factors influencing recall execution rates: severity, the number of cars to be recalled, recall execution rate, government policies, time since model launch, and recall costs, etc. As a component demand forecast model for automobile recalls, this study estimated the ARIMA model. ARIMA models were shown in three models: ARIMA (1,0,0), ARIMA (0,0,1) and ARIMA (0,0,0). These all three ARIMA models appear to be significant for all recall patterns, indicating that the ARIMA model is very valid as a predictive model for car recall patterns. Based on the classification of recall types, we drew some strategic implications for recall response according to types of recalls. The conclusion section of this research suggests the implications for several aspects: how to improve the recall outcome (execution rate), customer satisfaction, brand image, recall costs, and response to the regulatory authority.

A deep learning framework for building energy consumption forecast

Increasing global building energy demand, with the related economic and environmental impact, upsurges the need for the design of reliable energy demand forecast models. This work presents kCNN-LSTM, a deep learning framework that operates on the energy consumption data recorded at predefined intervals to provide accurate building energy consumption forecasts. kCNN-LSTM employs (i) k−means clustering – to perform cluster analysis to understand the energy consumption pattern/trend; (ii) Convolutional Neural Networks (CNN) – to extract complex features with non-linear interactions that affect energy consumption; and (iii) Long Short Term Memory (LSTM) neural networks – to handle long-term dependencies through modeling temporal information in the time series data. The efficiency and applicability of kCNN-LSTM were demonstrated using a real time building energy consumption data acquired from a four-storeyed building in IIT-Bombay, India. The performance of kCNN-LSTM was compared with the k-means variant of the state-of-the-art energy demand forecast models in terms of well-known quality metrics. It is also observed that the accurate energy demand forecast provided by kCNN-LSTM due to its ability to learn the spatio-temporal dependencies in the energy consumption data makes it a suitable deep learning model for energy consumption forecast problems.

An ecological stability-oriented model for the conjunctive allocation of surface water and groundwater in oases in arid inland river basins

Abstract With the continuous development of the population and social economy, the spatial and temporal distribution of water resources in arid inland river basins is severely uneven, and there is a sharp contradiction between agricultural water use and ecological water use. Irrational development and utilization of water resources has led to many problems, such as shrinking oases and drying lakes. To solve this problem, this study proposes a multiobjective, multiwater-source, ecological stability-oriented double-layer model for optimal allocation of water resources based on the large-scale system decomposition–coordination principle, the water balance principle, and a water supply and demand forecasting model. This model can resolve the contradiction between agricultural water use and ecological water use by optimizing and adjusting the crop planting structure, industrial structure, and the amount of water allocated to and groundwater level in each region and thereby achieve ecological stability and restoration of oases. The developed model was applied to the Heihe River Basin in an inland region of Northwest China. The long-term time series data of 2000–2016 were used to construct and calibrate the model. Finally, the practical ecological stability-oriented plan for conjunctive allocation of surface water and groundwater in different plan years was proposed. This model enriches the research results related to the conjunctive allocation of surface water and groundwater and provides a reference for the ecological restoration of oases in arid inland river basins.

The Drought Resilience Impact Platform (DRIP): Improving Water Security Through Actionable Water Management Insights

Millions of people living in the drought prone Horn of Africa face an increasing threat from a lack of safe, reliable and affordable water year-round as droughts become more severe and frequent. Drought emergencies emerge when reduced rainfall conspires with limited community water service capacity to cause dramatic reductions in access to water for people, livestock and agriculture. Drought-driven humanitarian emergencies can be prevented if groundwater is reliably made available at strategic locations during cycles of water stress. DRIP - The Drought Resilience Impact Platform, is an initiative combining early detection and planning with proactive groundwater management to ensure water availability, thus enabling drought-prone communities to become effective managers in the prevention of these humanitarian crises. It replaces reactive and expensive short-term assistance measures, like water trucking, with a framework for drought resilience. DRIP links in-situ sensors deployed in East Africa with remote sensing data to improve estimates for rainfall and groundwater availability, and will develop a localized model for water demand forecasting. These indicators support the operation and maintenance of strategically selected groundwater borehole systems, thereby helping to support water delivery during dry and drought seasons. DRIP can be used to support pay-for-performance contracting, ensuring that water asset management is incentivized. DRIP is presently monitoring the water supplies of about 3 million people in East Africa. This paper presents DRIP's current web-based functionality, which uses several custom and commercial tools, and DRIP's applications, including rainfall-adjusted indicators of water pump functionality in Kenya and Ethiopia. Future work includes experimental and statistical characterization of the impact of these capabilities on water security, and the development of forecasting capabilities. This work is supported NASA, USAID, and the National Science Foundation. The views expressed in this article do not necessarily reflect the views of the United States Agency for International Development or the United States Government.

Forecast of Traffic Vehicle Demand Based on AHP Decision Model

The development of high technology has driven the rapid growth of civil aviation passenger demand. However, most current studies ignore the impact of increased passenger traffic on ground vehicle demand. This paper studies the demand forecast of transportation vehicles in a specific environment. Taxis are under huge service pressure when faced with the characteristics of irregular time distribution and high dynamics. After the taxi driver escorts the passenger to the airport, it is a big problem whether to leave without a load or go to the car pool. This paper uses Analytic Hierarchy Process (AHP) to construct a judgment matrix, establishes an airport taxi demand forecast model based on the best interests. Taking the status of taxi operation in Kunming Changshui International Airport as an example, the results show that the model can predict taxi demand more accurately and has practical guidance significance. Compared with traditional schemes, it is more conducive to improving driver’s income.

New Product Demand Forecasting and Inventory Model towards Eyewear Cleaner Product: A Case Study of Nasho Company

Nasho Company is Nano-coating business based on chemicals. This business makes a product called "Nasho Self-cleaning liquid for Glasses" that give ability to protect the glass surface by water repellent, anti-fog, anti-dust, and anti-bacteria. This Company desires to do market expansion by finding an occasion to put the product in local optical stores in Bandung. Yet, the target that they already made needs to be cross-checked. On another hand, the productivity and efficiency of production in this Company are low due to order the material and produce the product only when demand comes. Production planning based on forecasting and inventory model is required for solving this problem. As a result, the Company is able to define their market expansion target as well as their material stocking. This research is using Structured Analogy Forecasting technique to define the forecast for new product and Fixed Order Interval with Safety Stock analysis for material stocking. Mix method is used in this research to gain information and identify the product that would be a benchmark in forecasting technique that in line with Inventory Model analysis. As a result of Structured Analogy forecasting, the benchmark product is Expo lenses cleaner using the judgmental process with thematic analysis and Single Exponential Smoothing with α= 1,92215 is applied with MAPE 6,42%, MAD= 59,68, and MSD= 5362,4, as well as demand in next year, would be 10631 bottles of lenses cleaner liquid. By 95% service level for Fixed Order Interval with Safety Stock, the Company would order; 14 l of Ethanol, 2,5 l Polydimethylsiloxane, 2 l of Isopropyl, 870 unit of the bottle, 13 boxes of wiper, 207 A3+ sheets of box, 25 A3+ sheets of the sticker, and 47 sheets of 1 m HVS of Instruction Paper in every 14 working days.

Design and realization of metering production plan management system

The metrology verification center is currently developing towards intelligent, unmanned and highly reliable. For the problem of inaccurate demand reporting of electric power company, metrology verification center heavy manual planning and low efficiency, the metrology production plan management system is developed. Firstly, metrology equipment demand forecast model is constructed by integrating all kinds of metrology equipment installation history data and policy factors. It realizes the accurate prediction of the demand for metrology equipment of electric power company and supported the metrology verification center to arrange verification production and purchase new meters. Secondly, during the production planning process, intelligent auxiliary method is used. Applying demand forecast results, through analysis and quantification of many factors that affect the preparation of plans, generates arrival plans, verification plans and distribution plans that cover the metrology verification center all production business processes. It improves planning efficiency, shortens the planning cycle and achieves overall control of production rhythm.

A Decentralized Informatics, Optimization, and Control Framework for Evolving Demand Response Services

This paper presents a decentralized informatics, optimization, and control framework to enable demand response (DR) in small or rural decentralized community power systems, including geographical islands. The framework consists of a simplified lumped model for electrical demand forecasting, a scheduling subsystem that optimizes the utility of energy storage assets, and an active/pro-active control subsystem. The active control strategy provides secondary DR services, through optimizing a multi-objective cost function formulated using a weight-based routing algorithm. In this context, the total weight of each edge between any two consecutive nodes is calculated as a function of thermal comfort, cost (tariff), and the rate at which electricity is consumed over a short future time horizon. The pro-active control strategy provides primary DR services. Furthermore, tertiary DR services can be processed to initiate a sequence of operations that enables the continuity of applied electrical services for the duration of the demand side event. Computer simulations and a case study using hardware-in-the-loop testing is used to evaluate the optimization and control module. The main conclusion drawn from this research shows the real-time operation of the proposed optimization and control scheme, operating on a prototype platform, underpinned by the effectiveness of the new methods and approach for tackling the optimization problem. This research recommends deployment of the optimization and control scheme, at scale, for decentralized community energy management. The paper concludes with a short discussion of business aspects and outlines areas for future work.

DEMAND FORECASTING FOR HIGH-TURNOVER SPARE PARTS IN AGRICULTURAL AND CONSTRUCTION MACHINES: A CASE STUDY

Conventional demand forecasting and inventory management models cannot be applied to replacement parts due to their intermittent and seasonal demand. Thus the aim of this study is to compare, in the case of the strategic stocking of high turnover replacement parts, the demand forecast model currently used by construction and agricultural machinery companies with the Box-Jenkins statistical model. The results show that it is important to use a methodology based on statistical techniques in inventory management, and that the proposed model adapts better to high turnover stock control.

Detection of base travel groups with different sensitivities to new high-speed rail services: Non-negative tensor decomposition approach

How many base travel groups (models) are necessary for clarifying the long-term day-to-day dynamics of intercity travel? In the past, several travel purposes (e.g., sightseeing, business, etc.) have been assumed. However, mobile-phone location data enables us to answer the above question because of their detailed time-series information. In this study, we propose a method for deriving the basic travel groups necessary for clarifying the time-series changes by applying nonnegative tensor factorization (NTF). This method is applied to the time-series data of several long-distance travelers to the Ishikawa prefecture, to where the Hokuriku High-speed rail (HSR) has been newly extended. Based on this, the number of base travel groups necessary for predicting the effect of the new HSR is estimated as twelve, which is greater than the number used in the previous demand forecasting models. The estimated groups include components that appear to correspond to different travel purposes (e.g., sightseeing, business, and homecoming), as in previous surveys. These results confirm that the methodology proposed in this study can clearly extract groups with different elasticities, due to the traffic service. The HSR effect can be clarified by dividing it into several characteristics and detailed components. In addition, if multiple HSR effects are analyzed, a more accurate demand-forecasting model for the new HSR service can be proposed.