Daily Demand Research Articles

Attention-deficit/hyperactivity disorder is associated with more alcohol problems and less substance-free reinforcement: A behavioral economics daily diary study of college student drinkers.

Behavioral economic theory suggests that alcohol risk is related to elevated alcohol reinforcing efficacy (demand) combined with diminished availability of reinforcing substance-free activities, but little research has examined these reward-related processes at the daily level in association with comorbid conditions that might influence behavioral patterns and reward. Young people with attention-deficit/hyperactivity disorder (ADHD) report high levels of risky drinking, and this risk may be due in part to elevated demand for alcohol and diminished engagement in enjoyable and valued substance-free activities. College student drinkers (N = 101; 48.5% female; 68.3% White; 18-22 years old) with (n = 51) and without (n = 50) ADHD completed 14 consecutive daily diaries (diary entry n = 1,414). We conducted a series of multilevel path models to examine (a) the associations among ADHD and average daily alcohol demand, substance-free enjoyment, and response contingent positive reinforcement (RCPR) for goal-directed behaviors; (b) the associations among concurrent daily alcohol demand, substance-free reinforcement, and RCPR for goal-directed behaviors and daily alcohol use and alcohol-related negative consequences; and (c) the moderating effect of ADHD on these within-day associations. ADHD was significantly associated with more daily alcohol-related negative consequences and less daily substance-free enjoyment and RCPR. Regardless of ADHD status, there were significant associations among behavioral economic risk factors and alcohol use and negative consequences, though effects differed within and between persons. There were no moderating effects of ADHD on within-person associations. Results expose areas of impairment specific to drinkers with ADHD and advance theory on ADHD and hazardous drinking. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Examining daily electricity demand and indoor temperature profiles in UK social housing flats retrofitted with heat pumps

Examining daily electricity demand and indoor temperature profiles in UK social housing flats retrofitted with heat pumps

The impact of photovoltaic self-consumption on the daily electricity demand in Spain: Definition of a model to estimate it

IntroductionA radical shift in energy production is underway worldwide, replacing fossil fuels with renewable sources and causing structural changes in power generation systems. Problem statementPhotovoltaic installations for self-consumption have experienced a steep increase in recent years. They have reached a significant installed capacity to cause a noticeable reduction in consumption from the national grid, which can cause serious management problems. ObjectivesIn this work, the evolution of the Spanish demand in the last years is analyzed to identify the influence of self-consumption in the overall demand. In addition, a mathematical model is defined to estimate this influence. MethodologyThe demand curves of equivalent days in years with high and low installed self-consumption photovoltaic systems have been compared. Then, an estimation of the electricity generated with this source is proposed, with a mathematical model that takes into account data on solar radiation, installed photovoltaic power for self-consumption and other relevant factors. ResultsThe analysis of the demand has shown a significant reduction of the electricity demand in daylight hours when the number of self-consumption photovoltaic systems increases. Moreover, the proposed model has been able to provide an estimation of the electricity generated with this source. The addition of these estimates to the actual consumption curves of years with a high number of self-consumption installations gives profiles close to those obtained when self-consumption was low. RecommendationNew storage systems need to be implemented and grid management need to be improved to take advantage of the surpluses produced by photovoltaic systems.

Assessing the Applicability of Solar Thermal Technologies for Industrial Tea Drying

Most process heat being used for various industrial applications is supplied from fossil fuels. Coal, gas, and wood fuel are the most used heat resources which usually power steam boilers for production of heat used in a wide range of industrial processes. Since economic growth is largely linked to industrial production, it implies that, a shift towards use of clean energy technologies in industries is crucial to meet the rising demand of process heat while, cutting down carbon emissions to mitigate climate change. Solar thermal systems have over the past years shown potential as a practical alternative to fossil fuels for production of heat for several applications including, water and space heating, industrial process heating and electricity generation in thermal power plants. This study seeks to assess the feasibility of employing solar thermal technologies for drying purposes at Tingamira-Tanganda Tea estate in Zimbabwe. Systems advisor model (SAM) was applied to simulate performance of linear Fresnel (LF) and parabolic trough (PT) solar thermal technologies for industrial tea drying. Information on daily heat demand, required process temperatures and current heat sources was obtained to inform the design process of the proposed solar thermal systems. LF system exhibited better techno-economic performance. It requires a smaller collector area (360 m2) with a lower initial capital cost which is about 8% cheaper. Moreover, it produces higher annual energy at lower levelized cost of heat for solar multiples higher than 1. Carbon footprint of the factory would be reduced by about 114 tonnes CO2 annually. Sensible thermal energy storage technology can be considered for back up heat and heat recovery.

Decoding the future: Proposing an interpretable machine learning model for hotel occupancy forecasting using principal component analysis

Accurate hotel occupancy forecasting is vital for optimizing hotel revenue, yet interpretable machine learning tools lack extensive research. This paper presents a two-step approach utilizing historical and advanced booking data. Principal Components Analysis (PCA) groups similar patterns in booking curves, followed by a pickup forecasting model to predict occupancy. Evaluating the approach using real booking data from three European hotels (2018–2022), it outperformed two benchmarks: classical additive pickup and clustering-based pickup methods. Empirical results demonstrate the superiority of PCA-based methods across all hotels and forecasting horizons. Additionally, incorporating Average Daily Rates into PCA enhances daily hotel demand forecasts, offering potential for enhanced predictions with business operational information in a low-dimensional space.

Financial Implications of Rooftop Solar Penetration in Sri Lanka from Utility Point of View

Solar is one of the fast growing and economical renewable power generation sources. The intention of development of solar power generation is to promote clean energy while addressing the basic requirement of uninterrupted and reliable electricity supply.The Sri Lankan government decided to adopt a policy that would use subsidies to encourage the growth of solar PV-based electricity. As a result, the market developed with a large number of customers eager to install solar rooftop systems, and they received strong assistance from solar power service providers. Hence, the utility is required to absorb the solar energy produced and this study focuses on the impact of solar penetration to the utility.System dispatch data for year 2019 and rooftop solar generation data for a period of 6 months of year 2019 were considered for the analysis in this paper. The model compares the financials of two scenarios to estimate the impact to the utility. The base case scenario, which is business as usual with the already installed solar generation in the system and No solar scenario which assumes that there is no solar in the system and therefore the demand is met by other available generation capacities in the system.This paper examines the two scenarios with careful calculations of how the system can still run without solar in the system. Daily demand patterns were also analysed and then expanded to months and summarized for the year. This research focuses the financial impact of solar electricity penetration to the national grid and how everyone can still be benefitted.

Estimating restaurants’ unconstrained demand: a systematic approach to reducing structural bias in forecast accuracy measures

研究目的技术从其他行业的传播以及厨房设备的创新推动了餐饮业内的结构变化。然而, 这种变化直接影响了评估需求预测准确性。本研究探讨了在餐饮业结构改变后，评估至关重要的需求预测准确性时所面临的令人独特和复杂性。研究方法本文自研了一个数学模型来描述和探讨评估需求预测准确性中的结构性偏差的本质。然后, 使用数值模拟构建一个市场示例, 以更好地了解上述偏差的特征。最后, 将这种预测准确性评估的系统性偏差与其他传统的餐饮业需求预测情境进行对比。研究发现本文概述了中央厨房运营中需求预测是动态的, 因此产生了结构性偏差的理论基础。更具体地说, 在使用中央厨房并集中订单的情境下, 本文发现需求预测直接设定了容量限制, 因此产生了在需求预测准确度衡量中的结构性偏差。依赖这样的预测准确性度量可能产生严重的负面商业结果。研究创新这项研究首次表明, 在中央厨房运营的独特的新环境中, 由于新的设定即每日菜品需求预测直接决定每日容量水平, 需求预测准确度衡量标准有着严重偏差, 长期来讲准确性可能下降, 从而导致次优的商业决策。本研究的主要理论贡献是提供一个餐饮企业在新运营环境中解释和描述需求预测准确度中结构性偏差的全新分析模型。

Planning of wind-photovoltaic-storage-hydrogen-water for a zero-carbon microgrid on an independent island

Due to low daily demand and the remote location from the shoreline, diesel generators dominate the energy supply on these islands, resulting in significant emissions of harmful gases. In this paper, a collaborative planning approach is proposed for a zero-carbon microgrid incorporating wind turbines (WTs), photovoltaic modules (PVs), electrochemical energy storages(EESs), hydrogen energy storages(HESs), and sea water desalination on an independent island. Long-term and short-term energy storage mechanisms, represented by HESs and EESs, respectively, are coordinated to harness the abundant wind and solar energy resources. Additionally, consideration is given to the requirement for sea water desalination to achieve a self-sustained equilibrium of zero-carbon water and electricity for the island’s inhabitants. Illustrative analysis demonstrates that the proposed planning method accounts for the stability and environmental preservation in terms of the planning and operation. This has profound implications for advancing the transformation of the island’s microgrid energy infrastructure, enhancing the utilization of renewable energy, and bolstering the self-sustaining capacity of hydropower resources.

Real Time Hotel Booking Demand Optimization

The significance of having proper revenue management and convenient operations in hotels rely on accurate daily demand. The main challenges that are being tackled conventionally are the forecasting of the number of cancellations and the calculation of the Average Daily Rate (ADR). One of the distinct characteristics of the hotel industry being so unstable is to adopt proactive strategies by dealing with a lot of external changes such as pandemics, disasters caused by nature, and economic fluctuations around the world. Booking cancellations are instrumental in helping hotel managers to optimise resources and inventory while ADR forecasting offers them equally essential projections concerning anticipated profit or loss margins. This research relies on several data modelling steps, including time series aggregation and decision merging, which are later followed by decomposition and model selection. SARIMAX and LSTM models are adopted for future traffic flow modelling, which demonstrates better forecasting performances. Binary classification is employed for feature engineering techniques together with model selection methods. Binary Classification is performed with a number of experiments with machine learning algorithms, AdaBoost turned out to be the best model which surpassed CART , KNN , Random Forest, Gradient boosting algorithms , and Light Gradient Boosting algorithms. The results of this are of great help to the hotel management for taking better decisions which are connected to the changing situations of the market

Daily tourism demand forecasting and tourists’ search behavior analysis: a deep learning approach

Daily tourism demand forecasting and tourists’ search behavior analysis: a deep learning approach

Impact of Various Ration Energy Levels on the Slaughtering Performance, Carcass Characteristics, and Meat Qualities of Honghe Yellow Cattle.

Consumers are increasing their daily demand for beef and are becoming more discerning about its nutritional quality and flavor. The present objective was to evaluate how the ration energy content (combined net energy, Nemf) impacts the slaughter performance, carcass characteristics, and meat qualities of Honghe yellow cattle raised in confinement. Fifteen male Honghe yellow cattle were divided into three groups based on a one-way design: a low-energy group (LEG, 3.72 MJ/kg), a medium-energy group (MEG, 4.52 MJ/kg), and a high-energy group (HEG, 5.32 MJ/kg). After a period of 70 days on these treatments, the animals were slaughtered and their slaughter performance was determined, and the longissimus dorsi muscle (LD) and biceps femoris (BF) muscles were gathered to evaluate meat quality and composition. Increasing the dietary energy concentration led to marked improvements (p < 0.05) in the live weight before slaughter (LWBS), weight of carcass, backfat thickness, and loin muscle area. HEG also improved the yield of high-grade commercial cuts (13.47% vs. 10.39%) (p < 0.05). However, meat quality traits were not affected by treatment except for shear force, which was affected by dietary energy. A significant improvement (p < 0.05) in the intramuscular fat (IMF) content was observed in the HEG. Little effect on the amino acid profile was observed (p > 0.05), except for a tendency (p = 0.06) to increase the histidine concentration in the BF muscle. Increasing dietary energy also reduced C22:6n-3 and saturated fatty acids (SFAs) and enhanced C18:1 cis-9 and monounsaturated fatty acids (MUFAs, p < 0.05). Those results revealed that increasing energy levels of diets could enhance slaughter traits and affect the meat quality and fatty acid composition of different muscle tissues of Honghe yellow cattle. These results contribute to the theoretical foundation to formulate nutritional standards and design feed formulas for the Honghe yellow cattle.

Utilization of Biomethane to Support Energy Security in Indonesia, Riau Province: A Case Study

As the largest global palm oil producer, Indonesia contributes 59% to the world’s palm oil supply, producing 46.9 million tons of Crude Palm Oil (CPO) in 2021. However, this extensive production poses an environmental challenge through Palm Oil Mill Effluent (POME), estimated at 156 million m3 annually. POME treatment facilities release Biogas, predominantly Methane (CH4) at 50%–75%, a potent Greenhouse Gas (GHG) with 21 times the global warming potential of CO2. Conversely, Methane is a key component of widely used Natural Gas. Properly treated, POME-derived biogas can be upgraded to Biomethane, a clean and carbon-negative alternative for the Natural Gas Grid or fossil fuel substitution, estimated at 1,280 MWe, equivalent to 288 MMSCFD CH4. Despite Biomethane’s potential, its utilization remains limited to self-use in Palm Oil Mills or power plants, even in Riau Province, home to Indonesia’s largest oil palm plantation. PT Perusahaan Gas Negara Tbk survey identified potential customers in Riau willing to adopt natural gas, hindered by infrastructure and economic scale challenges. This research analyzes obstacles to biomethane business development in Indonesia, focusing on Riau Province, aiming to formulate an optimal distribution scheme and sustainable strategies. Methodologically, a mix of quantitative and qualitative data collection techniques, including literature studies, interviews, and questionnaires, informed a comprehensive analysis employing geographical, transportation cost, financial modeling, SWOT, and risk analyses. Findings suggest trucking as the optimal distribution mode for biomethane to scattered customers. Economic viability hinges on biomethane volume, necessitating stability and increased sales volume strategies. For Riau, four potential customers with a total daily demand of 11,248.93 m3 can be served affordably by selecting palm oil mills within a 76 km radius. Price and security supply considerations are crucial. This research provides insights into overcoming challenges and optimizing the biomethane business in Riau, offering a sustainable model for broader adoption in Indonesia.

Influence of hot water consumption patterns on the viability of low-temperature solar thermal systems in the food industry

The thermal demand in agri-food industries is characterised by its considerable variability, hindering the implementation of Solar Water Heating Systems (SWHS). The main objective of the study is to provide an overview of how consumption patterns influence the profitability and energy savings of SWHS in the sector, while also identifying additional key variables that affect their viability. For this purpose, approximately 1,300,000 cases have been examined, considering a wide range of consumption patterns, economic variables, and locations. Demand seasonality, weekly frequency and energy prices exhibit the greatest impact on the feasibility of SWHS. Uniformity of daily demand, location and investment cost are also determinants. Thus, single-day-per-week consumption patterns make SWHS unviable in most scenarios. However, with only three non-consecutive days, feasibility increases significantly, achieving paybacks of less than 5 years and energy savings of over 40 % when conditions are highly favorable. In strongly seasonal industries, the viability of SWHS is seriously compromised. The payback in patterns with a peak consumption in the early morning can double the value with uniform one (e.g., from 4 to 8 years in the most favorable scenarios at a price of 0.1 €/kWh). The results tables reflecting payback and energy savings in thousands of different scenarios represent a valuable tool for decision-making in industries. By looking for the scenario with the most similar characteristics, it is possible to estimate the profitability and savings of the SWHS in a given industry, as well as the possible variations when changing the assumed variables.

Adaptation Mechanisms of Olive Tree under Drought Stress: The Potential of Modern Omics Approaches

Olive (Olea europaea L.) is a crop of enormous economic and cultural importance. Over the years, the worldwide production of olive oil has been decreasing due to various biotic and abiotic factors. The current drop in olive oil production resulting from climate change raises concerns regarding the fulfillment of our daily demand for olive oil and has led to a significant increase in market prices. In the future, there will be a higher chance that we will face a severe shortage of olive oil, which could harm both the economic sector and the food supply. As olive groves cover more than 5 million hectares in the European Union alone, the need to preserve the crop in the context of extreme climatic events is imperative. As drought is considered one of the most limiting factors in agriculture, drought-resistant varieties and sustainable irrigation strategies are being developed to mitigate the impact of drought on crop productivity and secure the future supply of olive oil. This review focuses on recently gained insights into drought stress in olive trees through omics and phenomics approaches to unravelling mechanisms that may lead to developing new varieties that are tolerant against drought elicited by changes in growing systems.

UAV-Based Remote Sensing to Evaluate Daily Water Demand Characteristics of Maize: A Case Study from Yuci Lifang Organic Dry Farming Experimental Base in Jinzhong City, China

Soil moisture critically influences crop growth, especially in dryland environments. Precise agricultural management requires real-time monitoring of stratified soil moisture and assessment of crops’ daily water needs. We aim to provide low-cost, high-throughput information acquisition services for dryland regions with underdeveloped infrastructure and offer scientific support for sustainable water resource management. We used UAVs (Unmanned Aerial Vehicles) with multi-spectral sensors for routine maize monitoring, capturing leaf reflectance. Constructing vegetation indices, we quantified the relationship between leaf water content and surface soil moisture, using the Biswas model to predict deep soil moisture distribution. We used UVAs to monitor crop height and calculated the daily water demand for the entire growth period of corn using the Penman Montes equation. We found an R2 of 0.8603, RMSE of 2.455%, and MAE of 2.099% between NDVI and canopy leaf water content. A strong linear correlation (R2 = 0.7510) between canopy leaf water content and soil moisture was observed in the top 20 cm of soil. Deep soil moisture inversion from the top 20 cm soil moisture showed an R2 of 0.9984, with RE mostly under 10%, but exceeding 20% at 120 cm depth. We also constructed a maize height model aligning with a sigmoidal growth curve (R2 = 0.9724). Maize’s daily water demand varied from 0.7121 to 9.4263 mm, exhibiting a downward-opening parabolic trend. Integration of rainfall and soil water data allowed for dynamic irrigation adjustments, mitigating drought and water stress effects on crops. We highlighted UAV multi-spectral imaging’s effectiveness in monitoring crop water needs, facilitating quick daily water requirement estimations. Our work offers a scientific foundation for managing maize cultivation in drylands, enhancing water resource utilization.

MENTAL HEALTH RESILIENCE OF MOTHERS CHILDREN WITH SPECIAL NEEDS

Families with children who have special needs encounter intricate difficulties in providing care and assistance to their children. The psychological well-being of mothers who provide care for children with special needs is a significant area of concern, given the pivotal role they play in the child's care and overall growth. The objective of this study is to comprehend the psychological fortitude of moms who breastfeed infants with special needs in Indonesia. In this study, a qualitative research methodology is employed, utilizing in-depth interview approaches to gather data from mothers who have children with special needs. The empirical evidence indicates that the resilience levels of these mothers exhibit considerable variation. Resilience is influenced by various elements, such as social support, awareness of the child's illness, and the capacity to cope with stress. These mothers exhibit resilience and the capacity to recover from daily demands and hardships. They depend on familial support, religious faith, companionship, and community, and cultivate efficient mechanisms for managing challenges. Despite experiencing significant levels of stress, these mothers demonstrate unwavering dedication to ensuring optimal care for their children.

Optimizing energy solutions: A techno-economic analysis of solar-wind hybrid power generation in the coastal regions of Bangladesh

Hybrid renewable energy systems have acquired attention worldwide for their ability to harness multiple renewable sources parallelly like solar, wind, and hydropower, presenting numerous advantages. Bangladesh is forced to rely on the traditional fossil fuel-powered power generation infrastructure in order to meet the nation’s increasing need for electricity. The goal of this paper is to improve the percentage of renewable energy in Bangladesh’s energy landscape by addressing the technical, economic, and environmental elements of building a specialized hybrid system at Patenga Sea Beach through a methodical approach. This study is the first to pinpoint and address Patenga Sea Beach’s limits in light of Shah Amanat International Airport’s nearby location in Chittagong. In order to build the suggested system, a daily load demand of 1000 kWh and an annual peak demand of 53.57 kW were taken into account. The analysis shows that this system has a levelized cost of energy (LCOE) of 0.03$, generating 4,604.591 MWh of power annually. Findings from HOMER reveal an initial capital outlay of $350,688 for initial capital investment, with an annual O&M cost of $3,821, contributing to its cost-effectiveness. During the span of its lifetime, the hybrid system can also avoid 5,767 tons of CO2. The simulation program PVsyst has been utilized to assess the PV system-specific performance, viability, and outcomes. According to the PVsyst evaluation, the PV system has an 89 % system performance ratio, a 0.017$ levelized cost of energy, and a break-even period of only 6.6 years. The sensitivity analysis examines renewable resources, factoring in climate change’s effects on solar irradiation, wind speed, replacement cost, and operational expenses. It validates the hybrid system’s viability across various environmental scenarios, demonstrating resilience to changes in renewable resource costs and availability. The suggested hybrid system can be placedto minimize costs associated with energy generation, circumvent structural and geographic limitations, improve the Patenga Sea beach’s visual appeal, and lower greenhouse gas emissions. In addition to supporting future decisions about the deployment of hybrid systems, this research could assist in the integration of sustainable and renewable energy sources into the national grid.

BEET: Blockchain Enabled Energy Trading for E-Mobility Oriented Electric Vehicles

Renewable Energy Sources (RESs) are gaining considerable attention to reduce human dependence on fossil fuels and minimize harmful gases in our surroundings. Existing literature on energy trading focused on providing renewable energy to smart homes, smart buildings, and smart offices to fulfill their daily energy demands obtained from RESs. Besides, Electric Vehicles (EVs) use either power grid energy or a battery exchange mechanism to recharge their low EV batteries. The continuous use of power grids to recharge low EV batteries causes a significant load on power grids. Due to this, power grids are inadequate to fulfill the ever-increasing demands of EVs in the future. In this context, we propose a Blockchain Enabled Energy Trading (BEET) framework oriented EV charging. A system architecture of the BEET framework is presented to describe the functioning of each layer and its associated entities. We formulate an optimization problem that maximizes the revenue in the energy trading process using a knapsack optimization. Smart contracts are designed on the consortium blockchain network to sell and buy renewable energy to aggregators and from producers, respectively. Moreover, an EV charging mechanism is designed to intelligently allocate renewable energy to consumers at a low price. A comparative analysis is performed with state-of-the-art works in terms of charging price, revenue, throughput, and latency. The results indicate that the BEET framework outperforms compared to state-of-the-art works to address the renewable energy demand problem to realize E-mobility. It is clarified that the data considered in the experimental analysis were obtained from statistical simulations in realistic E-Mobility environment settings.

Analysis of Sleep, Nocturnal Physiology, and Physical Demands of NCAA Women's Ice Hockey Across a Championship Season.

Merrigan, JJ, Stone, JD, Kraemer, WJ, Friend, C, Lennon, K, Vatne, EA, and Hagen, JA. Analysis of sleep, nocturnal physiology, and physical demands of NCAA women's ice hockey across a championship season. J Strength Cond Res 38(4): 694-703, 2024-The aims of this study were to evaluate the (a) relationships between daily physical demands and nighttime sleep, heart rate (HR), and heart rate variability (HRV); (b) weekly changes in physical demands and sleep; and (c) differences among positions and between training and competition during a competitive season in National Collegiate Athletic Association (NCAA) women's ice hockey. Twenty-five NCAA Division I women's ice hockey athletes wore a sensor at night to monitor sleep quantity or quality (e.g., time asleep and sleep efficiency) and physiology (e.g., HR and HRV). During training and competitions (31 regular season and 7 postseason), athletes wore performance monitoring systems to assess workload demands (e.g., training impulse and TRIMP). As internal workload (TRIMP, Time >80% of HRmax, Average HR) during training or competition increased, nocturnal HRV decreased, HR increased, and Sleep Duration, Sleep Score, and Readiness Score decreased that night. Across the season, athletes experienced lower HRV, but exhibited longer sleep durations. Training Distance, Duration, Time >80% HRmax, Average HR, and TRIMP decreased, whereas competition Total Distance, Duration, and TRIMP increased across weeks throughout the season. There were differences across positions and season blocks when evaluating these data at the mesocycle level. Athletes slept longer before competition compared with training, but physiological data did not differ. Competitions had greater physiological demands than training. We speculate that the increased focus on sleep hygiene, as evidenced by the increase in sleep over the season, may have served as a recovery aid to combat physiological stress of accumulated demands of competitions that increased over time into postseason tournaments.

Forecasting demands of blood components based on prediction models

BackgroundAn adequate blood supply is an important guarantee for saving lives and protecting health. In order to manage the blood supply more effectively when the condition of demand and supply are uncertainty, it is very important to forecast the demands of blood resources. Materials and methodsSARIMAX model and LSTM model were integrated into the prediction system of blood station. The collection and supply data of blood components was directly imported into the forecasting models to achieve automatic data update and model update. The forecasting daily demands of apheresis platelets, washing red blood cells (RBCs), suspended RBCs and plasma were recorded from January to June 2023 and compared with real data. ResultsThe prediction models had good forecasting performances. In the goodness of fit results of apheresis platelet model, the maximum value of coefficient of determination (R2) could reach 87.6%, and the minimum value of the mean absolute percentage error (MAPE) was only 0.0037. The predicted data of washing RBCs could be basically fitted, and the MAPE was 0.0121. For the prediction of suspended RBCs, the R2 was greater than 66%, and the MAPE could be 0.0372. The plasma model generated very high goodness of fit results, with R2 of over 90% and the lowest MAPE of 0.0394. ConclusionThe forecasting models, which predicts future demands of different blood components based on historical data, can help managers to overcome the challenges of blood stock control more effectively, thereby reducing blood waste and blood shortages.