Demand Estimation Research Articles

A simplified dynamic tool for building heating and cooling energy requirements estimation on a daily time scale

A persistent dilemma in building energy modeling consists of finding the proper trade-off between accurate results, data availability and limited modelling effort. In fact, quasi-steady state models require a limited number of inputs, however providing often rough results; dynamic tools are instead usually very accurate but involve a huge effort to obtain all the needed information and implementing it into the tool. In this context, we propose a simplified dynamic model for building heating and cooling demand estimation on a daily basis, according to the assessment of all the involved heat transfer mechanisms of the thermal zone. The required inputs are limited and comparable to those needed for other quasi-steady state models, but three tuning coefficients allow the simulation to be performed at the daily time scale. The proposed tool is here successfully tested on a wide set of test cases, differing for building structure, external climate, and occupants’ profile. In addition, reliable results are provided also in case of multi-year application and availability of a little amount of data for calibration, with average errors typically below 10% in daily cooling and heating energy requirement estimation, compared to benchmark demands. Finally, an example of application explores the application field of the tool.

Simulation of the use of rainwater in two schools in the public municipal education network in Aracaju-SE

With an increasing frequency periods of intense water scarcity are recorded in various parts of the planet. A measure that can be very efficient, is using rainwater, preserving potable water for use where it is needed, and contributing to the fight against urban floods. Considering the foregoing, this article aims to capture’s simulation and use of rainwater in two schools of the public municipal education network of Aracaju-SE, through the estimation of non-potable demand, estimation of the catchable volume, verification of the attendance of the estimated non-potable use and the impact of this action on the urban drainage system. Based on the roof area and the average’s data of precipitation, the volume of water captured by the roof was calculated, and finally, a demand for non-potable use was estimated, verifying if this demand is attended, in addition, the impact generated on the local urban drainage system was analyzed. The results indicate that: a) The Oviedo Teixeira School had an average demand of 258 m³, an average catchable volume of 326 m³ and an average reduction of 66% in the volume thrown into the drainage; b) The Anísio Teixeira School had an average demand of 60 m³, an average catchable volume of 140 m³, and an average reduction of 53% in the volume thrown into drainage. It is concluded that rainwater totally meets the estimated demand and rainwater harvesting positively impact on the drainage system, if used on a large scale.

Analyzing Brand-Level Chips Demand in the United States Using the Multinomial Logit Model

This study estimates demand for 52 chip brands using IRI scanner data. The multinomial logit model addresses dimensionality and endogeneity issues in demand estimation. All brands exhibit elastic demand, with own-price elasticities between -5.0412 and -1.4251, indicating high consumer responsiveness to price changes. Notably, tortilla chip brands are less elastic than potato chip brands. Baked chip brands fall under the category of highly elastic brands. Funyuns has the most elastic demand, while Calidad Triangle has the least elastic demand. Cross-price elasticities (0.0010 to 0.0263), exhibiting the IIA property, indicate that consumers have brand loyalty, as seen by comparisons with own-price elasticities' magnitudes.

Research on optimization layout of hydrogen refueling facility network based on renewable energy hydrogen production mode

Within the transportation sector, hydrogen fuel cell vehicles represent the most efficient method of harnessing hydrogen energy. The establishment of a hydrogen refueling facility network that encompasses the entire cycle of hydrogen energy production, transportation, and application contributes to the effective promotion of hydrogen fuel cell vehicles. However, existing research predominantly focuses on hydrogen production and the conversion of refueling stations, neglecting the economic and stability considerations of the full-cycle use of hydrogen energy. This study proposes a hydrogen refueling facility network planning model that utilizes hydrogen energy throughout its full cycle. Firstly, a hydrogen demand estimation model based on the application scenarios of hydrogen fuel cell vehicles which clarifies the hydrogen requirements in each scenario is proposed. Regional hydrogen energy needs and the costs of building hydrogen refueling stations are then considered, using a set cover model to optimize the overall layout of existing and new refueling stations. Finally, taking into account the supply costs of renewable energy hydrogen production points and regional supply stability, a hydrogen refueling facility network planning model is constructed. The Beijing-Tianjin-Hebei region is selected as a case study, establishing a large-scale hydrogen supply network based on renewable energy hydrogen production, transportation, and usage.

A novel approach for demand estimation under a flexible mixed logit model

This study explores customer heterogeneity and proposes a flexible mixed logit (FML) model without prior knowledge. We investigate a demand estimation problem under the FML model to estimate the choice probabilities of customers and identify the best-fitting mixing distribution for these probabilities. To address this problem, we introduce the loss-likelihood estimation (LLE) method to convert the estimation problem into a constrained convex problem and then design the Frank-Wolfe based (FWB) algorithm to solve the constrained convex problem and prevent model misspecification. This two-stage approach is referred to as the LLE-FWB approach. To validate this approach, we conduct a convergence analysis to calculate its convergence rate and perform numerical experiments to examine its effectiveness and robustness. The theoretical results show that the convergence rate of the LLE-FWB approach is O(1/k), and the simulation results show that this approach performs better, particularly for large data volumes and assortment sizes. Besides, we apply this approach to real-world data to support the implementation of personalized marketing strategies. The results show that the average expected revenue obtained in real-world instances is 17.42 % higher than the actual revenue.

Dynamic Demand Estimation in Auction Markets

Abstract We study demand estimation in a large auction market. In our model, a dynamically evolving population of buyers with unit demand and heterogeneous and privately known preferences for a finite set of differentiated products compete in a sequence of auctions that occur in discrete time. We define an empirically tractable equilibrium concept in which bidders behave as though they are competing with the stationary distribution of opposing bids, characterize bidding strategies, and prove existence of equilibrium. Having developed this demand system, we prove that it is non-parametrically identified from panel data. We extend the model to consider a random coefficients demand system akin to workhorse demand models in industrial organization, and show that this too is non-parametrically identified. We apply the model to estimate demand and show how large sellers can exercise market power by using persistent reserve price policies, which induce higher bids and, therefore, revenues. Our analysis highlights the importance of both dynamic bidding strategies and panel data sample selection issues when analysing these markets.

A Random Consideration Set Model for Demand Estimation, Assortment Optimization, and Pricing

Random Consideration Set Model We operationalize a microfounded consumer choice model—the random consideration set (RCS) choice model of Manzini and Mariotti [Manzini P, Mariotti M (2014) Stochastic choice and consideration sets. Econometrica 82(3):1153–1176]—that captures the limited attention of consumers, assuming that purchases are based on fixed preference orderings with consideration sets formed from independent attentions. We provide a condition for uniquely identifying model parameters and design an efficient algorithm for model parameters estimation. We offer a greedy-like algorithm for assortment optimization, adaptable for optimal assortment subject to cardinality constraint or discovering efficient sets. We extend the model to consider random product preferences, with a 1/2 performance-guaranteed approximation algorithm. Using data from a major U.S. airline, we find that the RCS model outperforms the mixed multinomial logit model in approximately half of the markets, particularly with smaller, less varied data sets.

Heteroskedastic supply and demand estimation: Analysis and testing

The Feenstra (1994) method is widely used in the international trade literature to estimate supply and demand elasticities. The method is mechanically an IV strategy, and we demonstrate that this has important implications for its application and reliability. The assumptions needed for it to yield unbiased estimates are stronger than previously understood, and in practice, estimates are subject to bias due to both weak instruments and violations of the exclusion restriction. We illustrate how these arise in context and show that standard tests identify estimates that are likely to be biased. In an application to U.S. import data, estimates of import demand and export supply elasticities are substantially lower among goods that pass standard tests relative to those that fail. We find evidence that this difference in elasticities reflects reduction of bias as well as some selection in the set of goods that tend to pass both tests.

Changes in rate-pressure product associated with pregnancy

In nonpregnant individuals, the rate-pressure product, the product of heart rate and systolic blood pressure, is used as a noninvasive surrogate of myocardial O2 consumption during cardiac stress testing. Pregnancy is considered a physiological cardiovascular stress test. Evidence describing the impact of pregnancy on myocardial O2 demand, as assessed by the rate-pressure product, is limited. This study aimed to describe changes in the rate-pressure product for each pregnancy trimester, during labor and delivery, and the postpartum period among low-risk pregnancies. This was a retrospective cohort study that assessed uncomplicated pregnancies delivered vaginally at term. We collected rate-pressure product (heart rate × systolic blood pressure) values preconception, during pregnancy for each trimester (at ≤13 weeks + 6/7 days, at 14 weeks + 0/7 days through 27 weeks + 6/7 days, and at ≥28 weeks + 0/7 days), during the labor and delivery encounter (hospital admission until complete cervical dilation, complete cervical dilation until placental delivery, and after placental delivery until hospital discharge), and during the outpatient postpartum visit at 2 to 6 weeks after delivery. We calculated the percentage change at each time point from the preconception rate-pressure product (delta rate-pressure product). We used a mixed-linear model to analyze differences in the mean delta rate-pressure product over time and the influence of prepregnancy age, prepregnancy body mass index, and neuraxial anesthesia status during labor and delivery on these estimates. Our cohort comprised 316 patients. The mean rate-pressure product increased significantly from preconception starting at the third trimester of pregnancy and during labor and delivery (P≤.05). The mean delta rate-pressure product peaked at 12% and 38% in the third trimester and during labor and delivery, respectively. Prepregnancy body mass index was inversely correlated with the mean delta rate-pressure product changes (estimate, -0.308; 95% confidence interval, -0.536 to -0.80; P=.008). In contrast, neither the prepregnancy age, nor neuraxial anesthesia status during labor had a significant influence on this parameter. This study validates the transient but significant increase in the rate-pressure product, a clinical estimate of myocardial O2 demand, during uncomplicated pregnancies delivered vaginally at term. Pregnant individuals with lower prepregnancy body mass index experienced a sharper increase in this parameter. Patients who receive neuraxial anesthesia during labor and delivery experience similar changes in the rate-pressure product as those who did not.

Estimating demand systems with corner solutions: the performance of Tobit-based approaches

ABSTRACT Since the introduction of the Tobit framework to perform estimation involving censored dependent variables, practitioners have been facing a clear trade-off between flexibility and theoretical plausibility in modelling consumers’ preferences in the presence of zero consumptions; the Kuhn-Tucker (or virtual price) approach is rigorously based on the economic choice theory but cannot be applied to complex and flexible demand systems, whereas the Tobit-based approach can be applied to any class of demand systems but is deficient in the theoretical foundations on the underlying preferences behind the observed choices. Hence, we assess the performance of three Tobit-based approaches (simple, correlated, and Amemiya-Tobin) and explore the extent of possible biases in elasticity estimates to provide reasonable criteria for model selection. Our analysis concludes that theoretical restrictions implied by the choice theory are essential to the Tobit model and improve its ability to capture the true underlying elasticities and mitigate overrejections. However, the performance of the Tobit models gradually deteriorates as the number of zero consumptions increases; the average rejection rate against the true elasticity values increases substantially as we have more zero consumptions. We illustrate the performance differences among the three Tobit models by applying them to the estimation of demand for fruits and vegetables.

Research Progress and Prospects of Public Transportation Charging Station Layout Methods

Electric buses have been vigorously promoted and implemented in major countries worldwide and have generated a huge demand for charging stations. Optimizing the daily charging experience of electric buses, adapting the daily operation scheduling, improving the utilization rate of charging stations, reducing the load on the power grid, and improving the operation efficiency of electric bus line networks require the reasonable layout of the charging stations. In this study, public transportation charging station layout and siting is the research object. We summarize the progress of analysis methods from the charging station and vehicle sides; introduce related research on the planning and layout of charging stations based on optimization models, including cost analysis and siting and layout for electric bus systems; summarize the data-driven station planning and siting research; and provide an overview of the current charging demand estimation, accuracy, and charging efficiency. Finally, we address the problems of the charging demand estimation accuracy, the mismatch between the charging station layouts for electric buses, and the charging demand on a long time scale. We suggest that research be conducted on data fusion for the temporal and spatial refinement of charging demand prediction in the context of the electrification of public transportation systems and the big data of telematics.

Estimation of Environmental Rebound Effect Induced by Shared Automated Passenger Transport Service in a Mid-Size European City via Microsimulation

The introduction of autonomous vehicles will revolutionize transport in urban and rural areas. Nevertheless, before we allow autonomous vehicles to roam our streets, we should strive to predict the impact that they could have and prevent as many negative externalities as possible. Moreover, it is expected that the ability to let go of the wheel and multitask could substantially decrease the in-vehicle value of time, triggering travel behavioral changes, which could in turn have a negative impact on the environmental sustainability of the transport system. Thus, we tried to estimate the environmental rebound effect linked to behavioral changes caused by shared autonomous vehicle deployment. This study presents results of a simulation of the transport system of Santander (Spain), performed by linking the activity-based demand estimation developed in SimMobility to microsimulation in Aimsun and its battery consumption and pollutant emissions models. The results yielded by the study present the magnitude to which identified travel behavioral changes could affect the environmental performance of the transport system, as well as the overall outcome of all identified behavioral changes. The outcomes show that the rebound effect could increase the CO2 emissions by almost 40% compared with a scenario with no behavioral changes. We believe this topic to be particularly interesting for policy makers, urban planners and regional authorities.

Electronics Spare Part Goods Demand Forecasting Using Markov Model

Customer demand forecasting plays an essential part in inventory management in retail companies. Accurate customer demand forecasting will increase a company's competitiveness and play a crucial role in making the right decisions for inventory management. Without demand estimation, products are often purchased more than needed, resulting in overstock or understock product storage in the warehouse. In this paper, we present the results of the Markov Chain method for predicting the quantity of demand for goods in the future to assist decision-making regarding the procurement of commodity goods within the company, especially in the procurement of electronics spare parts in retail companies. This study aims to develop a software-based forecasting system for retail companies using the Markov Model with predictive capabilities. The system will also provide purchasing quantity recommendations to fulfill the stock, calculated from the minimum stock and forecasted demand from customers and suppliers. Using the Markov Chain model, this study predicts electronics spare parts demand using data on item sales during 2022 in a retail company. The forecasting of electronics spare part demand resulted in 88.2% accuracy. The software-based forecasting system has been implemented and tested using black box testing. The testing result shows that the Markov forecasting system is feasible and can be used as a reference in providing electronics spare parts purchasing recommendations for retail companies.

Power demand estimation techniques applied to microgrid

ABSTRACT The increasing global demand for power necessitates a focus on renewable energy sources to reduce environmental impact and diversify energy generation. Estimating power demand in real-time electrical systems is crucial in this context. However, the literature contains various estimation techniques without clear guidance on the most suitable option based on performance and field-specific use. To address this gap, a study compares these techniques and identifies the most effective method for power demand estimation through a training process that assesses the accuracy of predictions against actual values. This comparison is conducted using the JUPYTER platform and historical data from the ENTSO-E power systems database. Results indicate that Linear Regression and Support Vector Machine techniques are commonly used for model evaluation, but the Adaptive Neural Network consistently performs as the most robust estimation method. The choice of technique depends on researcher preference, but it's essential to note that inadequate data preparation can lead to suboptimal results. In summary, as the world's power demand rises, the study identifies the Adaptive Neural Network as a promising tool for accurate power demand estimation, emphasizing the significance of proper data preparation in this process.

Forecast sales volume of aviation fuel in Jakarta using ARIMA Method to support inventory control

The civil aviation industry in Indonesia experienced a significant increase from 2009-2018. The increase occurred in passenger and cargo activities. However, the situation changed drastically when the Covid-19 pandemic occurred throughout the world in 2019. PT xyz as a company need to adapt and optimize the business process. Inventory management is something that can be optimized. The aim of this research was to forecast aviation fuel demand in Jakarta and utilize the forecast result as a basis for inventory management. This research utilizes time series data on aviation fuel sales to determine demand estimates for the following month. The method used to forecast is the Autoregressive Integrated Moving Average. Furthermore, the forecast data is combined with information on storage capacity and historical data on product supply arrivals as input data in calculating safety stock to obtain more economical inventory management. The result of this research is that the best forecast avtur demand in Jakarta is using Arima (1,1,1). Safety inventory calculation based on demand forecasting with 90% cycle service level for Jakarta is 42,495 kiloLiter and PT xyz still can reduce products in their storage worth IDR. 341.8 M in Jakarta to maintain the same cycle service level.

How does multi‐modal travel enhance tourist attraction accessibility? A refined two‐step floating catchment area method using multi‐source data

AbstractIn the post‐pandemic era, tourism has emerged as the most popular form of recreation in densely populated areas, driven by the pursuit of improved quality of life and the younger generation's growing enthusiasm for connecting with nature or history. Accurate measurement of the accessibility of tourist attractions has become crucial due to this trend. However, existing methods for measuring accessibility, such as the widely used two‐step floating catchment area (2SFCA) method, show limitations in multi‐modal transport networks as it overlooks multi‐modal travels and assumes uniform access for each transport mode within the catchment area. In this article, we present a novel multi‐modal network‐based two‐step floating catchment area (MMN‐2SFCA) method, which incorporates a super‐network to explicitly model the multi‐modal travels. The proposed method emphasizes the modeling of transfer behavior, resulting in a more comprehensive and accurate measurement of tourist attraction accessibility compared to the conventional 2SFCA method. The proposed method emphasizes the modeling of transfer behavior, travel mode choice, and travel demand estimation by multi‐source big data (including mobile phone signaling data, travel survey data, subway smart card data, and bike‐sharing data), resulting in a more comprehensive and accurate measurement of tourist attraction accessibility compared to the conventional 2SFCA method. The results of the case study in Suzhou (a famous tourism city in China) demonstrate the effectiveness of the proposed MMN‐2SFCA method. The method can rectify the imbalance in accessibility distribution caused by considering only single‐modal trips, and avoid overestimation of accessibility by accounting for transfer behavior. This study contributes to advancing accessibility measurement for multi‐modal transport networks. Moreover, the MMN‐2SFCA method offers excellent extensibility, enabling authorities to optimize and coordinate multi‐modal transport networks for improving the accessibility of tourist attractions and other facilities.

Single vs dual source surface energy balance model based actual evapotranspiration estimation

The current study aims to inter-compare the performance efficiency of the single and the dual source surface energy balance modeling approaches, namely EEFlux and SETMI, respectively for real time catchment scale - crop water demand estimations. For this, the afore-stated two surface energy balance modelling approaches were applied on the Narmada Canal Project, Sanchore, Rajasthan, India for estimating catchment scale actual evapotranspiration (ETa) values for the Rabi cropping seasons of the years 2013-14 and 2018-19, after incorporating the basic satellite data derived inputs viz. Land use, Land surface temperature and Gridded weather data. Due to the non-availability of the catchment scale ground based daily reference evapotranspiration (ETo) values for the study area, the Global Land Data Assimilation System based gridded meteorological data product was utilized, as a substitute for obtaining observed actual evapotranspiration (ETa) values for the investigated Rabi seasons of the study area. These actual evapotranspiration values were compared with those estimated through the single source, EEFlux and the dual source, SETMI modelling approaches to ascertain their comparative performance efficiency through the use of the five statistical indices viz. Mean Absolute Error, Root Mean Square Error, Mean Bias Error, Nash-Sutcliffe Efficiency and the Index of Agreement. The investigations revealed almost at par performance of the two modelling approaches. However, it was concluded that in contrast to the more detailed dual source approach i.e., SETMI, the simple single source approach i.e., EEFlux seemed to be more promising due to its user-friendly implementation and input data automation.

A data-driven framework for fast building energy demand estimation across future climate conditions

Abstract The rapid and precise forecasting of building energy requirements plays a crucial role in decision-making processes for architects during the early design phase. This study introduces a data-driven framework capable of projecting energy demands in the context of evolving climate conditions. We evaluated four widely-used machine learning algorithms. Our results indicated that a hybrid approach, integrating Catboost and Bayesian optimization, excelled in both accuracy and efficiency for predicting building energy demand under climate change conditions. The framework proposed herein has potential applications in fostering sustainability in early-stage architectural design.

Household electricity consumption determinants in major Nigeria cities

The increasing epileptic electricity supply, mainly in the residential areas of Nigerian cities, has been linked to the incorrect knowledge of the numerous socio-economic and physical indices that influence household electricity usage. Most of the seemingly identified explanatory factors were done at macro level which does not give a clear estimate of this electricity demand. The thrust of the study is to analyse empirically the household electricity determinants in Nigerian cities with a view to evolving a more informed and sustainable energy policy decision. Multistage area cluster sampling method was adopted in the study where 769 copies of structured questionnaire were distributed to electricity users of prepaid meters in five major Nigerian cities. The research hypothesis was tested using the multiple linear regression statistical tool. The result revealed that nine variables which include age (r = 0.05, p-value: 0.05), household income (r = 0.00, p-value: 0.05), number of hours that people stay outside the house (r = 0.043, p-value: 0.05), number of teenagers at home, (r = 0.006, p-value: 0.01) number of electrical appliances (r = 0.016, p-value: 0.01), type of house (r = 0.012, p-value: 0.01), hours that the electrical appliances are used (r = 0.043, p-value: 0.05), weather condition, (r = 0.011, p-value: 0.05) and the location of the building (r = 0.045, p-value: 0.05) were significant in determining the household electricity consumption. Policies based on the findings will give energy and urban planners an empirical basis for accurate and robust forecasting of the determinants that influence household electricity consumption in Nigeria that is devoid of any speculation or unfounded predictions.

Sizing isolated mini-grids in Kenya: Risk transfer to deal with multidimensional uncertainties and constraints

Isolated mini-grids (MG) can be an efficient option for rural electrification worldwide. Nonetheless, a large share of MG fail after a few years and inadequate sizing has been identified as a major risk. Academia, public authorities and funding agencies tend to consider the sizing of mini-grids mostly from a technical and economic angle, looking to optimize performance for MG developers and operators with tools such as HOMER. This paper proposes a different approach. We study the strategies adopted by different MG stakeholders to deal with their own uncertainties and constraints in the sizing process. Based on field work in Kenya, we detail how MG funders and regulators transfer risks to private MG developers and operators. As a result, the latter face risks regarding demand estimation, funding and regulatory aspects when sizing MGs. In turn, they adapt their methods and business models, sometimes transferring risks to end users. While flexible sizing might be a solution, we show that regulatory and funding issues limit MG modularity, leading low-income customers to eventually bear the consequences of ill-suited sizing.