Demand Model Research Articles

Performance analysis of different control models for smart demand–supply energy management system

Several features of innovative grid technologies can be deployed to improve the overall performance of the power system environment. This can be seen from the generation to the consumption of energy. The two-way communication of smart metering introduces the novel functionalities of the energy management system. This paper presents a practical implementation of using the intelligent metering system. It consists of implementing a nanogrid that optimally coordinates the energy from the solar panel, battery storage and utility grid to supply the end user. The developed model is validated with an optimal value of the state of charge of the distributed energy storage to maximise energy from the solar panel and battery storage while minimising the power received from the utility grid. A demand response scheme is employed to formulate the performance index of the energy management system using three optimal control models: adaptive open-loop control, adaptive closed-loop control and model predictive control schemes. The formulation of the performance index of each approach is a function of the energy flow from different resources depending on the power consumption. The three models have given different insights into the performance of the smart nanogrid, which may be used to the advantage of the grid owner or end user. Through the performance of the optimal strategies, it can be observed that energy management is ensured, and real-time monitoring of the entire system is guaranteed. The performance models facilitate the minimisation of the power from the utility, resulting in savings between 23.7% and 39.240% of the total energy demand from the end user. Besides, the system design is validated by an electrical system to form a real-world innovative nanogrid application in residential environments.

Optimizing demand forecasting for business events tourism: a comparative analysis of cutting-edge models

PurposeGiven the unique nature of business events tourism, this paper evaluates the forecasting performance of various models using search query data (SQD) to forecast convention attendance.Design/methodology/approachThis research uses monthly and quarterly business event attendance data from both the U.S. (Las Vegas) and China (Macau) markets. Using SQD as the input, we evaluated and compared the cutting-edge forecasting models including Prophet and Long Short-Term Memory (LSTM).FindingsThe study reveals that Prophet outperforms complex neural network models in forecasting business event tourism demand. Keywords related to convention facilities, conventions or exhibitions, and transportation are proven to be useful in forecasting business travel demand.Practical implicationsProphet is an accessible forecasting model for event-tourism practitioners, especially useful in the volatile business event tourism sector. Using verified search keywords in models helps understand traveler motivations and aids event planning.Originality/valueOur study is among the first to empirically evaluate the performance of forecasting models for business travel demand. In comparison with other mainstream forecasting models, our study extends the scope to examine both the U.S. and Chinese markets.

A ‘differential’ differential demand system: an application to US meat demand*

ABSTRACT I present a ‘differential’ (threshold) Rotterdam demand model. I argue that a standard model, which holds parametric relationships constant across all sizes of shocks to prices and expenditures, may produce misleading elasticity estimates. This arises because consumers may react less or even not at all to small shocks to prices and expenditures. However, when shocks are large enough to exceed any adjustment or transactions costs, consumers may react more strongly, thereby producing more elastic responses. I estimate the demand for meats in the US over the 1980–2019 period. I consider four meat products – beef, pork, chicken, and turkey – and assume that meats are weakly separable from other demand decisions. I confirm that reactions to larger shocks will result in more elastic price and expenditure adjustments. A test of the difference in the regimes delineated by the size of shocks to prices and expenditures is statistically significant.

Mandatory nutrition attributes labeling and consumer demand: a structural approach analysis of the US soft drink market

While soft drinks stand out as a major cause of obesity and overweight worldwide, the USA is the country most concerned with this problem. To reverse the situation, experts have agreed that labels and taxes represent promising policy tools. Focusing on the soda market, this paper investigates how consumer demand for nutrient contents responds to the US revised nutrition facts label policy implemented in 2020. Relying on retail scanner data, the study finds after estimation of a discrete choice logit demand model that the change in nutrition labels caused consumers to modify their purchasing behavior. However, they did not systematically switch from unhealthy beverages to healthy ones. The analysis finds evidence that the label revision policy was mostly impactful in decreasing consumers’ preference for soda with superhigh calories and high sugar content. Surprisingly, the joint effect of label and tax did not decrease the sampled consumers’ preference for unhealthy nutrient contents. They still preferred buying soft drinks superhigh in calorie content inside US cities with a tax on sugar-sweetened beverages (SSB), despite the revision of the facts label. Finally, the new nutrition label has improved the average surplus of consumers and total welfare. However, the gain is lower within the cities implementing the sugar tax.

REAL ANALYTIC DISCRETE CHOICE MODELS OF DEMAND: THEORY AND IMPLICATIONS

We demonstrate that a large class of discrete choice models of demand can be approximated by real analytic demand models. We obtain this result by combining (i) a novel real analytic property of the mixed logit and the mixed probit models with any distribution of random coefficients and (ii) an approximation property of finite mixtures of Gumbel and Gaussian distributions. To illustrate some of the implications of this result, we discuss how real analyticity facilitates nonparametric and semi-nonparametric identification, extrapolation to hypothetical counterfactuals, numerical implementation of demand inverses, and numerical implementation of the maximum likelihood estimator.

A Cross-Country Study of Cigarette Affordability and Single-Stick Purchases Using Survey Data From African Countries.

Reducing cigarette affordability is paramount for reducing cigarette consumption. Measuring affordability requires data on cigarette prices. Unlike the commonly used retail price of a 20-pack of the most-sold cigarette domestically, survey-derived cigarette prices reflect differences arising from the brand variety and the types of packaging in which cigarettes are purchased. This paper uses self-reported cigarette price data from the eight African countries that have implemented the Global Adult Tobacco Survey (GATS) to construct country-level Relative Income Prices. The relationship between cigarette affordability, cigarette smoking prevalence, and cigarette smoking intensity, is examined using logit models for smoking participation (N = 51122) and generalized linear models for conditional cigarette demand (N = 2443). GATS data are also used to produce nationally representative estimates of the prevalence of single-stick cigarette purchases in the sampled countries. The estimated affordability elasticity of cigarette smoking participation is -0.245 (95% CI = -0.411 to -0.078). The estimated affordability elasticity of smoking intensity is -0.155 (95% CI = -0.286 to -0.023). Single-stick cigarette sales dominate all-markets. The proportion of smokers who reported buying cigarettes in the form of single sticks during their most recent cigarette purchase exceeds 90% in Kenya, Tanzania, and Uganda. The results point to the need for governments in the countries sampled to increase excise taxes in a manner that renders cigarettes less affordable over time, and to enact and enforce legislation that prohibits the sale of single cigarettes. These findings highlight that measures to reduce both the demand and supply of cigarettes will be required to reduce their use in the region. This study is the first to examine the prevalence of single-stick cigarette purchases, and the association between cigarette affordability and smoking outcomes, in the African setting, using data from the GATS. Findings provide local evidence for the countries sampled, which represent over half of sub-Saharan Africa's adult population (aged 15 and older), on the importance of implementing excise tax increases that reduce cigarette affordability over time. They also highlight the need to enact and enforce legislation that prohibits the sale of single cigarettes.

PODE: privacy-enhanced distributed federated learning approach for origin-destination estimation

The statewide consumer transportation demand model analyzes consumers’ transportation needs and preferences within a particular state. It involves collecting and analyzing data on travel behavior, such as trip purpose, mode choice, and travel patterns, and using this information to create models that predict future travel demand. Naturalistic research, crash databases, and driving simulations have all contributed to our knowledge of how modifications to vehicle design affect road safety. This study proposes an approach named PODE that utilizes federated learning (FL) to train the deep neural network to predict the truck destination state, and in the context of origin-destination (OD) estimation, sensitive individual location information is preserved as the model is trained locally on each device. FL allows the training of our DL model across decentralized devices or servers without exchanging raw data. The primary components of this study are a customized deep neural network based on federated learning, with two clients and a server, and the key preprocessing procedures. We reduce the number of target labels from 51 to 11 for efficient learning. The proposed methodology employs two clients and one-server architecture, where the two clients train their local models using their respective data and send the model updates to the server. The server aggregates the updates and returns the global model to the clients. This architecture helps reduce the server’s computational burden and allows for distributed training. Results reveal that the PODE achieves an accuracy of 93.20% on the server side.

Non-functional demand model vs functional demand model: prospects for competitiveness in a transforming world

The purpose of the article is to analyse the problem of changing the relationship between non-functional and functional demand. The main task of the presented research is to define the most competitive model (based on functional or non-functional demand) of consumer behaviour in the conditions of the economy of the 20s of the XXI century. To solve this problem, we studied the structure of consumer demand: functional and non-functional ones. We identified the features of non-functional demand, and considered the reasons for its expansion in modern society. Moreover, paper highlights fixing changes in consumer orientations, which resulted in the search for satisfaction not only of material needs, but also aesthetic, emotional, and social ones. It allowed us to formulate a conclusion on the existence of objective reasons for the spread of non-functional demand. Within the framework of non-functional demand, we perform an analysis of origin and expansion of the downshifting phenomenon through the transformation of the basis of functional demand. The authors believe that qualitative changes in global socio-economic development have revolutionized the theoretical concepts of rationality, which are the basis of the functional demand model. Nevertheless, authors criticise the interpretation of downshifting as a model of irrational consumer behaviour, and put forward a hypothesis on the legitimacy of this new phenomenon to be analysed within the framework of functional demand. The authors consider the assumption of the possibility of changing the objectives of society to be a condition for the realization of this hypothesis. it can cause the transformation of «efficiency» concept over the past 300 years. Paper forecasts increasing of non-economic goals rationality, cooperation, mutual benefits, and the coupling of knowledge and technology to moral, cultural, and ethical principles and norms. As a result, in the contrary to the widespread assessment of downshifting as a deviant form of consumer behavior, there was put forward hypothesis considering downshifting as a part of classical consumer choice matrix, taking into account the transformation of this matrix itself.

Enabling efficient regional seismic fragility assessment of multi‐component bridge portfolios through Gaussian process regression and active learning

AbstractRegional seismic fragility assessment of bridge portfolios must address the embedded uncertainties and variations stemming from both the earthquake hazard and bridge attributes (e.g., geometry, material, design detail). To achieve bridge‐specific fragility assessment, multivariate probabilistic seismic demand models (PSDM) have recently been developed that use both the ground motion intensity measure and bridge parameters as inputs. However, explicitly utilizing bridge parameters as inputs requires numerous nonlinear response history analyses (NRHAs). In this situation, the associated computational cost increases exponentially for high‐fidelity bridge models with complex component connectivity and sophisticated material constitutive laws. Moreover, it remains unclear how many analyses are sufficient for the response data and the resulting demand model to cover the entire solution space without overfitting. To deal with these issues, this study integrates Gaussian process regression (GPR) and active learning (AL) into a multistep workflow to achieve efficient regional seismic fragility assessment of bridge portfolios. The GPR relaxes the probability distribution assumptions made in typical cloud analysis‐based PSDMs to enable heteroskedastic nonparametric seismic demand modeling. The AL leverages the varying standard deviation to select the least but most representative bridge‐model‐ground‐motion sample pairs to conduct NRHA with much‐improved efficiency. Both independent and correlated multi‐output GPRs are proposed to deal with bridge portfolios with seismic demand correlations among multiple components (column, bearing, shear key, abutment, unseating, and joint seal). Considering a single benchmark highway bridge class in California as the case study, the AL‐GPR framework and the associated component‐level fragility results are investigated in terms of their efficiency, accuracy, and robustness. The fragility results show that 70 AL‐selected samples would enable the GPR to derive bridge‐specific fragility models comparable to the ones using the multiple stripes analysis approach with 1950 ground motions considered for each individual bridge. The AL‐GPR model also successfully captures the physics of how bridge span length, deck area, column slenderness, and steel reinforcement ratio would change the damage state exceedance probabilities of different bridge components. The efficiency of AL stems from the fact that, with the multi‐output independent GPR, a stable and reliable fragility model can be achieved using 50 AL‐selected samples compared to at least 270 randomly chosen samples. The proposed methodology advances the state of the art in enabling more efficient and reliable regional seismic fragility assessment of multi‐component bridge portfolios.

A Cognitive Approach for Modeling Customer Demand Dynamics for Optimal Product Release Strategies

Abstract As agile processes are increasingly adopted for product design and as consumer preferences are rapidly evolving with increasing information available from digital media, there is a need for a demand model that can accommodate the dynamics of product development. However, existing models of demand estimation, such as the discrete-choice models, do not capture the dynamics of product development and decision-making processes and thus are unable to effectively capture the effect of product updates and the release of information. To address this gap, we present a dynamic demand model and demonstrate how it can be used to determine the optimal time to release product updates. The demand model is based on decision field theory (DFT), which enables the modeling of the dynamic behavior of human decision makers. The contributions of this paper are as follows. First, we formulate a computational model for demand modeling built on DFT and demonstrate the viability of using the model to determine product release strategies. Second, we provide analytical approximations of the demand model and compare the accuracy of the approximated demand against the demand predicted by the dynamics model. Third, we show an example of a game played by competitors trying to optimize demand for their products by choosing the optimal update time relative to each other. Finally, we demonstrate the feasibility of parameter estimation using only the demand data.

The Janus model of money demand

AbstractWe derive a new theoretical model of money demand, which reflects backward‐ and forward‐looking preferences in consumption and utility. The former aspect is captured by standard habit formation, and the latter by anticipation of future consumption preferences. By introducing both of these into the Ramsey–Sidrauski framework, we unveil a new highly general model of money demand. We discuss the model in light of the apparent breakdown of money‐demand functions and diminished policy relevance of monetary aggregates.

Dynamic balancing method of power distribution and consumption tasks based on state iterative prediction and resource peaking shifting

In the context of the new power system, the widespread access to massive distributed new energy sources has led to the power distribution and consumption tasks characterized by multiple time scales, wide random distribution, and large demand differences, resulting in unpredictable resource peaks in the tasks computing resource demand curve. In view of this situation, a method of forecasting and dynamic balancing of computing resource demand for power distribution and consumption tasks based on state iteration was proposed: firstly, the tasks computing resource demand model was established under the analysis of the attributes and parameter demand of the power distribution and consumption tasks scenario. Secondly, on the basis of the short-term effectiveness prediction of the traditional Markov model, the first-order difference of the state is used for data training to track the state fluctuation, and the historical state and the predicted state are used for state iteration, so as to avoid the convergence of long-term prediction. Finally, a dynamic balancing model is established according to the time-scale characteristics of cyclical and non-cyclical tasks, and the optimal configuration of load imbalance is achieved through the identification and adjustment of historical data and burst data. The simulation results show that the improved Markov model based on first-order difference and state iteration has the short-term accuracy of the traditional model and the long-term traceability of data fluctuations. The dynamic balancing model can combine the characteristics of historical data and burst data to effectively reduce the imbalance of resource demand, and show good ability to cope with resource imbalance deviation.

Determinants and spatial spillover effects of inbound tourism in Chinese cities

Purpose By using a wide range of macro and micro factors, this paper aims to provide a new assessment of the recent literature on inbound tourism demand models. Design/methodology/approach This study examines the determinants and spatial effects of inbound tourism using Hausman–Taylor and spatial econometric models. Findings Several important factors were identified, including local economic growth, openness to the outside world, regional size, geographic distance, foreign direct investment, level of innovation and average annual temperature. In addition, the study found strong cross-city competition effects on tourism resource endowment and hotel infrastructure. Originality/value Inbound tourism is a crucial link in achieving high-quality economic development. However, previous studies have mainly focused on the analysis of single influencing factors, ignoring the spatial spillover effects of factors.

The effect of visa types on international tourism

Visa policies are among the most important administrative formalities influencing international tourism flows. This study estimates a structural gravity model for tourism demand to quantify the effect of different types of visas. Furthermore, the effect of the monetary cost of the visa and changes in visa policies are evaluated. Thus, two novel databases on visa requirements are used. Our empirical analysis considers worldwide tourism movements, including domestic flows, from 194 origin countries to 163 destinations from 2016 to 2021. The results indicate that imposing visa requirements decreases international tourism movements, especially in developed countries, although the effect depends on the type of visa. Moreover, we find a low elasticity for the monetary cost of the visa.

Time Aggregation in Health Insurance Deductibles

Health insurance plans increasingly pay for expenses only beyond a large annual deductible. This paper explores the implications of deductibles that reset over shorter timespans. We develop a model of insurance demand between two actuarially equivalent deductible policies in which one deductible is larger and resets annually and the other deductible is smaller and resets biannually. Our model incorporates borrowing constraints, moral hazard, midyear contract switching, and delayable care. Calibrations using claims data show that the liquidity benefits of resetting deductibles can generate welfare gains of 3–10 percent of premium costs, particularly for individuals with borrowing constraints. (JEL D15, D82, G22, G51, G52, I13)

Variety-Based Congestion in Online Markets: Evidence from Mobile Apps

In many online markets, consumers have to spend time and effort browsing through products. The addition of new products could make other products less visible, creating congestion externalities. Using Android app store data, I take advantage of a natural experiment—a redesign of part of the store—to show evidence of congestion externalities online: more apps in the market directly reduce per app usage/downloads. The natural experiment also increases long-run entry, but a structural demand model that accounts for congestion externalities suggests that 40 percent of consumer variety welfare gains are lost from higher congestion. (JEL D12, D22, D62, L86)

The battery swapping service network deployment problem: Impact of driver range anxiety and impatience

The burgeoning field of electric vehicles (EVs) presents new challenges for the deployment and operation of urban battery swapping service networks (BSSNs), particularly in terms of driver behavior and dynamic demand, areas that are often overlooked in research. This paper offers a new perspective on BSSN deployment, focusing on customer churn. We develop a dynamic demand model considering driver range anxiety. Subsequently, we create a queue model to describe the battery swap process, factoring in impatient customers. We also examine the impact of customer ’balking’ and ’reneging’ on the battery swap process. Furthermore, we develop a queueing network algorithm framework to address this issue. A comparative analysis demonstrates the versatility of this method and its compatibility with heuristic algorithms. We propose two primary strategies to enhance service efficiency and decrease customer churn: increasing battery stocks and adding battery swapping stations (BSSs). Moreover, we investigate the application ranges of these strategies. We further observe that while delayed charging can curtail costs and balance grid loads, it can also cause considerable customer churn. Therefore, cost- and efficiency-oriented service providers should implement different strategies for the optimal deployment and operation of BSSNs.

Optimal earthquake intensity measures for seismic vulnerability assessment of concrete-faced rockfill dams

Earthquake intensity measure (IM) selection is crucial for developing probabilistic seismic demand models (PSDMs). This study explored the optimal IMs that can be utilized in PSDMs for concrete-faced rockfill dams (CFRDs). A two-dimensional nonlinear dynamic analysis of a CFRD was performed to assess its response to earthquake ground motion. A numerical model of CFRD was developed using the commercial software LS-DYNA with an advanced hysteretic soil model. A total of 20 earthquake IMs were selected for regression analysis. The regression analysis was performed between IMs and the damage index (DI). The IMs were evaluated through a selection process based on the goodness of fit as well as their efficiency, practicality, and proficiency. Accordingly, a range of optimal IMs was determined. The results indicated that effective design acceleration (EDA) is the best earthquake IM correlating with the seismic response of dam settlement and can be considered the optimal IM, whereas peak ground displacement (PGD), ratio of peak ground velocity to peak ground acceleration (PGV/PGA), and predominant period (Tp) are the less efficient. The fragility curves and surfaces were developed employing both scalar and vector IMs for CFRD.

Dynamic pricing strategies for efficient inventory management with auto-correlative stochastic demand forecasting using exponential smoothing method

The research presents a novel approach to inventory modelling, emphasizing stochastic demand and dynamic pricing strategies for a seasonal sales framework. The methodology divides the sales season into intervals, each associated with distinct pricing strategies influenced by stochastic factors. The study employ exponential smoothing for demand forecasting, optimizing inventory replenishment and dynamic pricing strategies through developed algorithms. Notably, the study determine the optimal smoothing parameter for demand forecasting, balancing responsiveness to recent demand patterns with long-term stability. Proposed research achieves a comprehensive framework empowering businesses to enhance competitiveness and profitability by addressing challenges of stochastic demand and dynamic pricing. Dynamic pricing strategies outperformed classical strategies, allowing businesses to respond promptly to demand fluctuations and optimize profit margins during sales seasons. Incorporating stochastic demand models enabled organizations to implement safety stock and buffer inventory levels effectively, mitigating risks associated with uncertain demand patterns. Real-time data analysis was crucial for adjusting price dynamics and making effective management decisions, leading to improved financial performance. The iterative nature of dynamic pricing strategies emphasized the importance of continuous refinement to adapt to evolving market dynamics. This approach enables data-driven decisions, adaptation to market fluctuations, and improved inventory management despite unpredictable demand. Ultimately, this study provides valuable insights and methodologies applicable across industries for efficient and profitable inventory management.

Dynamic Price Dispersion of Seasonal Goods in Bertrand–Edgeworth Competition

The primary distinguishing characteristics of various revenue management systems include fixed capacities, uniform products, and customers' sensitivity to pricing when making purchase decisions. Even with only two or three competitors, the competition remains intense. This study examines the sub-game perfect Nash equilibrium of price competition within an oligopoly market where goods are perishable assets. Each seller possesses a single unit of a non-replenishable good and competes by setting prices to sell their inventory over a finite sales period. Each period, buyers seek to purchase one unit of the good, and the number of buyers entering is random. All sellers' prices are visible to buyers, and there is no cost associated with searching for goods. Through stochastic dynamic programming methods, sellers' optimal responses can be determined by treating the competition as a one-time price-setting game, considering the remaining sales periods and the current demand pattern. Assuming a binary demand model, it is demonstrated that the duopoly model exhibits a unique Nash equilibrium. In addition, it is shown that the oligopoly model does not result in price dispersion based on a discussed metric. Moreover, when a generalized demand model is considered, the duopoly model features a unique Nash equilibrium with mixed strategies, whereas the oligopoly model exhibits a unique symmetric Nash equilibrium with mixed strategies.