Stochastic Forecasts Research Articles

A stochastic optimization procedure to design the fair aggregation of energy users in a Renewable Energy Community

Optimizing unit sizes and operation within a Renewable Energy Community (REC) can match intermittent renewable energy generation with variable user energy demands. These uncertain variables are often represented by pre-defined stochastic scenarios, without searching for the “best” scenarios and testing the optimization models with these scenarios. Moreover, little work both optimized RECs under uncertainty and distributed optimal life-cycle costs (investment and operation) among members. Thus, the objectives are: i) identifying the “best” set of stochastic scenarios of solar irradiance and user electricity demands and ii) assessing the accuracy of the “stochastic forecasts” of the total system costs and unit sizes, obtained by solving a stochastic programming model based on the “best” scenarios. The proposed novel procedure shifts the “present moment” back in time to split historical data into “past” and “future” periods used to identify the “best” scenarios and compare the “stochastic forecasts” with the utopic “perfect forecasts” based on the perfect knowledge of real data, respectively. The small errors between these forecasts in the optimal life-cycle costs (less than 2 %) and sizes (3–13 %) indicate good effectiveness of the suggested procedure. Also, the optimal life-cycle costs of “stochastic forecasts” are fairly distributed among users by applying the Shapley value mechanism.

Forecasting Population in an Uncertain World: Approaches, New Uses, and Troubling Limitations

AbstractThe long human lifespan enables long run forecasts of population size and age distribution. New methods include biodemographic research on upper limits to life expectancy and incorporation of early experiences affecting later life mortality such as smoking, obesity, and childhood health shocks. Some fertility forecasts incorporate education and quantum‐tempo insights. Statistical time series and Bayesian methods generate probabilistic forecasts. Yet recent decades have brought surprising changes in the economy, natural environment, and vital rates. In these changing circumstances we need new methods and the increasing use of probabilistic models and Bayesian methods incorporating outside information. The increasing use of microsimulation combined with aggregate forecasting methods is a very promising development enabling more detailed and heterogeneous forecasts. Some new uses of stochastic forecasts are interesting in themselves. Probabilistic mortality forecasts are used in finance and insurance, and a new Longevity Swap industry has been built on them. Random sample paths used to generate stochastic population forecasts can stress‐test public pension designs for fiscal stability and intergenerational equity. Population forecasting a few decades ago was a dull backwater of demographic research, but now it is increasingly important and is full of intellectual and technical challenges.

Fatigue Crack and Residual Life Prediction Based on an Adaptive Dynamic Bayesian Network

Monitoring the health status of aerospace structures during their service lives is a critical endeavor, aimed at precisely evaluating their operational condition through observation data and physical modeling. This study proposes a probabilistic assessment approach utilizing Dynamic Bayesian Networks (DBNs), enhanced by an improved adaptive particle filtering technique. This approach combines physical modeling with various predictive sources, encompassing cognitive uncertainties inherent in stochastic predictions and crack propagation forecasts. By employing crack observation data, it facilitates predictions of crack growth and the residual life of metal structure. To demonstrate the efficacy of this method, the research leverages data from three-point bending and single-edge tension fatigue tests. It gathers data on crack length during the fatigue crack progression, integrating these findings with digital twin theory to forecast the residual fatigue life of the specimens. The outcomes show that the adaptive DBN model can precisely predict fatigue crack propagation in test specimens, offering a potential tool for the online health assessment and life evaluation for aerospace structures.

Implementing policies and predictive stochastic models to restrict borderline personality disorder’s access to restricted medications: comorbidity with factitious disorder, functional neurological disorder and medically unexplained symptoms

IntroductionWe are facing increased access to hospital beds and increased use of restricted medications by people with borderline personality disorder (BPD). Our former research shows BPD comorbidity with factitious conditions, functional neurological disorder and medically unexplained symptoms. We also registered that persons with BPD might craft or exaggerate symptoms to access restricted medications. In the worst cases, they might share these medications (benzodiazepines, hypnotics, and anxiolytics) with street values for profit or other recreational purposes.ObjectivesTo generate forecasting models and preventive policies to deal with BPD factitious disorders and improve the effectiveness of the UK National Healthcare Service (NHS) in reducing unnecessary admissions to general and psychiatric hospitals. More selective policies will capture and discourage BPD’s feigning and exaggerating symptoms for accessing restricted medications.MethodsThe underlying analysis framework is stochastic forecasting. We used current knowledge and data to complete systematic future predictions extracted from recent trends. A logical-mathematical model generated the required expressions. We identify four major model components to be introduced in the model: BPD (A), factious disorders (B), prescribing restricted medications (C), antisocial behaviours (D), and access to hospital beds (E).ResultsThe Boolean expression becomes [A then B then C then D then E], or [A ⇒ (B ⇒ (C ⇒ (D ⇒ E))] with a truth density of 96.875% (Figure 1).ConclusionsBPD should alert healthcare of the risks of symptom exaggeration and factitious mental diseases. These conditions are used to access often restricted medications, such as benzodiazepines, sleep tablets, and anxiolytics, for personal and communal use. Street sharing of these last increases local criminality. In worst cases, a hospital bed is granted without preventive triage. The risk is the indoor access to these medications. We advocate policies for the discontinuation of community prescription of these drugs.Disclosure of InterestNone Declared

Implementing policies and predictive stochastic models to attend to borderline personality disorder crises: the dysthymia-suicide cycle

IntroductionUK healthcare is undergoing significant challenges in facing borderline personality disorder (BPD) and accommodating the increased demand to allocate sufficient care and carers to deal with BPD’s growing number and emotional and suicidal crises.ObjectivesTo generate forecasting models and preventive policies to deal with BPD crises and improve the effectiveness of the UK National Healthcare Service in suicide prevention (NHS).MethodsThe underlying analysis framework is stochastic forecasting. We used current knowledge and data to complete systematic future predictions extracted from recent trends. A logical-mathematical model generated the required expressions. The software for logic prediction and annotation was Wolfram Alpha (Wolframalpha.com).Results Persons with BPD become suicidal because the team cannot comprehend and address the cycle of dysthymia, rumination and suicide. The BPD crises start from Stage 1 (α), assessing the comorbidity between BPD with dysthymia, cyclothymia, autism and ADHD. Teams shall avoid overmedication as ineffective. Stage 2 (β) is introspection and rumination, which do not respond to pharmacotherapy. The health carers establish if rumination is present and suggest distraction techniques. Stage 3 (γ) is when constant rumination with catastrophising leads to hopelessness. Stage 4 (δ) is when BPD starts feeling more anxious, depressed and unable to stop rumination. We suggest thought-stopping techniques and discourage social isolation, which triggers rumination. As BPDs use external locus of control and aim for higher dosages of antidepressants and anxiolytics with minimal effect, we explain that medication is not the only solution. Stage 5 (ε) is a crisis and panic attack because constant rumination brings back traumatic thoughts focused on the past, present and future. This is when BPDs self-refer to the hospital, attempt suicide, and feel that hospital admission is the only solution. The stages combined generated Model I. The Model II forecast Δ from this study is that we will observe a higher frequency (Δ) of hospital occupancy (Δbo = A), suicidal attempts (Δsa = B), and heavy service use (Δsu = C) by BPDs.ConclusionsThe predictive model algorithm has thus extracted (1) Model I (Analysis): [α → (β → (γ → (δ → ε)))] = Z; The truth density for Model I and its strength of prediction for stage progression is 96.87% in the dysthymia-rumination-suicide cycle; and (2) Model II (Prediction): Z implies (A And B And C), Z→A ∩ B ∩ C; the truth density for the Model II is 56.25% for predicting a national shortage of healthcare resources. The combined models predict a truth of 73.81% in the outcomes of BPD crises in the UK NHS due to the dysthymia-suicide cycle.Disclosure of InterestNone Declared

Implementing policies and predictive stochastic models to attend to borderline personality disorder crises: rationalising ssri antidepressants prescription in suicide prevention

Introduction We are facing increased suicide attempts and deliberate self-harm from persons with borderline personality disorder (BPD) who are also on antidepressants, multiple antidepressant prescriptions and antidepressant augmentations. Our previous observations suggest that antidepressants might increase suicide attempts in those on this medication and who have BPD. The absent response to antidepressants is due mainly to the comorbid dysthymia, cyclothymia, rumination, autism and ADHD in BPD.ObjectivesTo generate forecasting models and preventive policies to deal with BPD crises and improve the effectiveness of the UK National Healthcare Service (NHS) in suicide prevention.MethodsThe underlying analysis framework is stochastic forecasting. We used current knowledge and data to complete systematic future predictions extracted from recent trends. A logical-mathematical model generated the required expressions. The software for logic prediction and annotation was Wolfram Alpha (Wolframalpha.com). The four parameters for stochastic predictions are, BPD (A), antidepressant No. 1 (B), antidepressant No. 2 (C), and suicide attempts (D). Boolean function metrics can help analyse the impact and truth of forecast modelling with truth density.ResultsThe logic expression for suicide prediction due to liberal antidepressant prescribing is Ψ = A intersects B, intersects C, intersects D; that is,. Ψ = A ∩ B ∩ C ∩ D, which yields a Boolean truth density of 6.25%. The truth table always has a positive outcome as long as any of the factors exist except when none is present.ConclusionsThe predictive Boolean function and truth table suggest that suicide presentation is predictable if there is a prescribing of one or more antidepressants in BPD and if there is an antidepressant augmentation or dose maximisation. We speculate that SSRI antidepressants block self-regulatory mechanisms of fear of death while triggering impulses to self-harm and suicide from overstimulation of SSRI receptors. Without fear mechanisms, death by suicide is felt as not terrifying.Disclosure of InterestNone Declared

Stochastic Time Series Analysis, Modeling, and Forecasting of Weekly Rainfall Using Sarima Model

Rainfall holds critical significance for water resource applications, particularly in rainfed agricultural systems. This study employs the Autoregressive Integrated Moving Average (ARIMA) technique, a data mining approach commonly used for time series analysis and future forecasting. Given the increasing importance of climate change forecasting in averting unexpected natural hazards such as floods, frost, forest fires, and droughts, accurate weather data forecasting becomes imperative. The objective of this study was to develop a Seasonal Auto-Regressive Integrative Moving Average (SARIMA) model for forecasting weekly rainfall in Junagadh Station, Gujarat. Utilizing 53 years of historical data (1963 to 2016), the SARIMA model predicts weekly rainfall for the subsequent five years (2018 to 2022). Through comprehensive evaluation using ACF and PACF plots, AIC, SBC, MAPE, and MAE values, the study identifies SARIMA (0,0,4)(0,1,1)52 as the optimal model, offering the most accurate prediction. The robust results affirm that the SARIMA model provides reliable and satisfactory weekly rainfall predictions. This research contributes valuable insights into the precision and efficacy of SARIMA models for rainfall forecasting, aiding in strategic water resource management in the Junagadh region.

Stochastic Forecasting of Regional Age-Specific Fertility Rates: An Outlook for German NUTS-3 Regions

Regional fertility forecasts are important for long-term planning in a variety of fields that include future birth numbers in their forecast, such as school or kindergarten planning. They are one of the major components of regional population forecasts as well. Therefore, it is important to construct reliable forecasts that are based on sophisticated models that cover the high complexity of future regional fertility. We suggest a novel forecast model for forecasting regional age-specific fertility rates that covers long-term trends by time series models, demographic and regional correlations by principal component analysis, and future uncertainty by Monte Carlo simulation. The model is applied to all German NUTS-3 regions (districts/Kreise) simultaneously, where we forecast all regional age-specific fertility rates through the period of 2022–2045. The results from the simulations are presented via median predictions with 75% prediction intervals of the regional total fertility rates. The simulation shows strong regional heterogeneities in long-term fertility trends that are associated with the historical background of Germany, housing supply for families, opportunities for education, and the strength of labor markets, inter alia.

Stochastic forecasting of variable small data as a basis for analyzing an early stage of a cyber epidemic

Security Information and Event Management (SIEM) technologies play an important role in the architecture of modern cyber protection tools. One of the main scenarios for the use of SIEM is the detection of attacks on protected information infrastructure. Consorting that ISO 27001, NIST SP 800-61, and NIST SP 800-83 standards objectively do not keep up with the evolution of cyber threats, research aimed at forecasting the development of cyber epidemics is relevant. The article proposes a stochastic concept of describing variable small data on the Shannon entropy basis. The core of the concept is the description of small data by linear differential equations with stochastic characteristic parameters. The practical value of the proposed concept is embodied in the method of forecasting the development of a cyber epidemic at an early stage (in conditions of a lack of empirical information). In the context of the research object, the stochastic characteristic parameters of the model are the generation rate, the death rate, and the independent coefficient of variability of the measurement of the initial parameter of the research object. Analytical expressions for estimating the probability distribution densities of these characteristic parameters are proposed. It is assumed that these stochastic parameters of the model are imposed on the intervals, which allows for manipulation of the nature and type of the corresponding functions of the probability distribution densities. The task of finding optimal functions of the probability distribution densities of the characteristic parameters of the model with maximum entropy is formulated. The proposed method allows for generating sets of trajectories of values of characteristic parameters with optimal functions of the probability distribution densities. The example demonstrates both the flexibility and reliability of the proposed concept and method in comparison with the concepts of forecasting numerical series implemented in the base of Matlab functions.

Community stochastic domestic electricity forecasting

The domestic sector is a significant energy consumer – accounting for around 40% of global electricity demand – due to household demand diversity and complexity. An accurate and robust estimation of domestic electrical loads, environmental impacts, and energy-efficiency potential is crucial for optimal planning and management of energy systems and applications. However, uncertainties resulting from simplistic socio-technical attributes, microclimatic variations, and oversimplification of the effects of interdependent variables make domestic energy modelling challenging. In this research, a hybrid bottom-up community energy forecasting framework is developed to estimate sub-hourly domestic electricity demand using a combination of statistical and engineering modelling approaches by considering key factors influencing household consumption, including demographic characteristics, occupancy patterns, and the features, ownership, and utilisation patterns of electric appliances. The framework is tested on a community in Wales, UK and validated on an annual, daily, and sub-hourly basis with monitored electricity usage averages derived from the UK Energy Follow-Up Survey and the sub-national electricity consumption datasets. Results closely reflect annual and daily household demand at individual dwellings and aggregated levels, with an estimation accuracy of up to 90%. Moreover, the framework facilitates more reliable sub-hourly demand profiles compared to conventional simulation practices that overestimate daily electricity demand and sub-hourly peaks by up to 15% and 50%, respectively.

Quantifying uncertainty in probabilistic volcanic ash hazard forecasts, with an application to weather pattern based wind field sampling

Probabilistic forecasting of volcanic ash dispersion involves simulating an ensemble of realistic event scenarios to estimate the probability of a particular hazard threshold being exceeded. Although the number of samples that make up the ensemble, how they are chosen, and the desired threshold all set the uncertainty of (or confidence in) the estimated exceedance probability, current practice does not quantify and communicate the uncertainty in ensemble predictions. In this study, we use standard statistical methods to estimate the variance in probabilistic ensembles and use this measure of uncertainty to assess different sampling strategies for the wind field, using the example of volcanic ash transport from a representative explosive eruption in Iceland. For stochastic (random) sampling of the wind field, we show how the variance is reduced with increasing ensemble size and how the variance depends on the desired hazard threshold and the proximity of a target site to the volcanic source. We demonstrate how estimated variances can be used to compare different ensemble designs, by comparing stochastic forecasts with forecasts obtained from a stratified sampling approach using a set of 29 Northern European weather regimes, known as Grosswetterlagen (GWL). Sampling wind fields from within the GWL regimes reduces the number of samples needed to achieve the same variance as compared to conventional stochastic sampling. Our results show that uncertainty in volcanic ash dispersion forecasts can be straightforwardly calculated and communicated, and highlight the need for the volcanic ash forecasting community and operational end-users to jointly choose acceptable levels of variance for ash forecasts in the future.

A Stochastic Load Forecasting Approach to Prevent Transformer Failures and Power Quality Issues Amid the Evolving Electrical Demands Facing Utilities

New technologies, such as electric vehicles, rooftop solar, and behind-the-meter storage, will lead to increased variation in electrical load, and the location and time of the penetration of these technologies are uncertain. Power quality, reliability, and protection issues can be the result if electric utilities do not consider the probability of load scenarios that have not yet occurred. The authors’ approach to addressing these concerns started with collecting the electrical load data for an expansive and diverse set of distribution transformers. This provided approximately two-and-a-half years of data that were used to develop new methods that will enable engineers to address emerging issues. The efficacy of the methods was then assessed with a real-world test dataset that was not used in the development of the new methods. This resulted in an approach to efficiently generate stochastic electrical load forecasts for elements of distribution circuits. Methods are also described that use those forecasts for engineering analysis that predict the likelihood of distribution transformer failures and power quality events. 100% of the transformers identified as most likely to fail either did fail or identified a data correction opportunity. The accuracy of the power quality results was 92% while allowing for a balance between measures of efficiency and customer satisfaction.

Stochastic modelling and forecasting of relative humidity and wind speed for different zones of Kerala

The variations in climatic conditions depend on seasonal changes throughout the year. Modelling and prediction of climatic conditions can help to determine the impacts of seasonal changes in climate over a specific period of time. Climate change can directly and indirectly affect agricultural, industrial, geographical and technological sectors in our society. Agriculture and the allied sector are seriously affected by changes in climate since it leads to complete destruction of cultivated crops. In this study, in order to model and forecast relative humidity and wind speed for northern, central and southern zones of Kerala, stochastic approach using SARIMA (Seasonal Autoregressive Integrated Moving Average) model was employed. The monthly weather data for the northern zone and the central zone of Kerala was taken from the location of RARS Pilicode and RARS Pattambi for a period of 39 years (1982-2020) whereas for southern zone, data was collected from the location of RARS, Vellayani for a period of 36 years (1985-2020) with the help of data access viewer. The model validation was done using MSE (mean square error), RMSE (root mean square error), MAE (mean absolute error) and RMAPE (relative mean absolute percentage error). The RMAPE values of relative humidity and wind speed in different zones of Kerala was less than 10 per cent which indicated that fitted model is showing accurate performance. The best selected SARIMA model is used in attaining anticipated values of relative humidity and wind speed for the next 5 years.

AD-STGCRN: A Model for Traffic Flow Prediction

Aiming at the problem that the existing prediction models fail to utilize the serial correlation, temporal correlation and spatial correlation of data from multiple perspectives to achieve accurate prediction, a dual-mode spatiotemporal graph convolutional neural network model combined with attention mechanism is proposed. This paper has been tested and validated in very stochastic traffic forecasting. experiments on two publicly available traffic datasets, PeMSD04 and PeMSD08, show that AD-STGCRN has higher prediction accuracy, with the model’s prediction errors reduced by an average of 18%, 16% and 25% for MAE, RMSE and MAPE respectively on PeMSD04 compared to other prediction models. On PeMSD08 they were reduced by an average of 18%, 16% and 20% respectively.

Stochastic Nonlinear Model Predictive Control for a Switched Photovoltaic Battery System

Battery systems gain popularity among users in residential household setups. In this setup, currently the main source of profitability is to increase photovoltaic (PV) self-sufficiency which is highly dependent on the battery system efficiency. We present a control approach based on stochastic dynamic programming (SDP) suitable to increase the system efficiency. The optimization framework includes a switched system with standby losses, a nonlinear modeling of the converter losses as well as a stochastic forecast model for household load and PV generation. We show in a simulation of a typical benchmark case that our approach can in fact reduce overall system losses and costs of operation. Then, the applicability in a real-world scenario is shown using a commercially available battery system in a field test.

Rotation in age patterns of mortality decline: statistical evidence and modeling

In the context of mortality forecasting, “rotation” refers to the phenomenon that mortality decline accelerates at older ages but decelerates at younger ages. Since rotation is typically subtle, it is difficult to be confirmed and modeled in a statistical, data-driven manner. In this paper, we attempt to overcome this challenge by proposing an alternative modeling approach. The approach encompasses a new model structure, which includes a component that is devoted to measuring rotation. It also features a modeling technique known as ANCOVA, which allows us to statistically detect rotation and extrapolate the phenomenon into the future. Our proposed approach yields plausible mortality forecasts that are similar to those produced by Li et al. [Extending the Lee-Carter method to model the rotation of age patterns of mortality decline for long-term projections. Demography 50 (6), 2037–205, and may be considered more advantageous than the approach of Li et al. in the sense that it is able to generate not only static but also stochastic forecasts.

О получении стохастических прогнозов в детерминированной модели банковской системы России

In this article we propose the method of obtaining forecasts in stochastic terms for deterministic models. The proposed method is computationally simpler than the one used in dyna­mic stochastic general equilibrium (DSGE) models. The method is based on the estimation of parameters in the deterministic paradigm and the estimation of the vector of sample means and the covariance matrix for the increments of exogeneous variables on the in-sample period. For every realization of exogeneous variables, the trajectories of endogenous variables is calculated. The methods of mathematical statistics such as moments calculation, construction of confidence intervals, testing various hypotheses can be applied to them. The approach is illustrated on the model of the Russian banking system, which successfully reproduces the wide set of its indicators. Several interesting properties of the obtained stochastic forecasts were found, including the violation of their normality and the nontrivial dynamics of confidence intervals. Several scenarios of key rate and exchange rate resembling their actual dynamics in the beginning of the 2022. Several conclusions on the influence of key rate and exchange rate on the basic indicators of the banking systems are made. In particular, several effects are found which could not be discovered in the purely deterministic modelling paradigm.

Parsimonious stochastic forecasting of international and internal migration on the NUTS-3 level – an outlook of regional depopulation trends in Germany

Substantiated knowledge of future demographic changes that is derived from sound statistical and mathematical methods is a crucial determinant of regional planning. Of the components of demographic developments, migration shapes regional demographics the most over the short term. However, despite its importance, existing approaches model future regional migration based on deterministic assumptions that do not sufficiently account for its highly probabilistic nature. In response to this shortcoming in the literature, our paper uses age- and gender-specific migration data for German NUTS-3 regions over the 1995–2019 period and compares the performance of a variety of forecasting models in backtests. Using the bestperforming model specification and drawing on Monte Carlo simulations, we present a stochastic forecast of regional migration dynamics across German regions until 2040 and analyze their role in regional depopulation. The results provide evidence that well-known age-specific migration patterns across the urban-rural continuum of regions, such as the education-induced migration of young adults, are very likely to persist, and to continue to shape future regional (de)population dynamics.

Stochastic mortality forecasts for Bangladesh.

Mortality forecasts are essential part for policymaking in any aging society. In recent years, methods to model and forecast mortality have improved considerably. Among them, Lee-Carter method is one of the most influential method. In this paper, Lee-Carter method is applied to forecast mortality and life expectancy of Bangladesh. A functional data analysis approach is used to decompose the smoothed log-mortality rates in Lee-Carter framework for higher goodness-of-fit of the models and for longer forecast horizons. Bangladesh has been experiencing a mortality transition and has gained life expectancy in last few decades. The fitted model here showed higher pace of mortality decline for women in Bangladesh than that of men. The forecasts showed continuation of mortality improvement in long run and by 2060 life expectancy at birth is expected to reach over 80 years for both sexes in Bangladesh. The study also predicts the effect of reduction in infant mortality on the life expectancy in Bangladesh.

What can we obtain from the initial conditions of assimilating wind tower observation to improve near-surface meteorological elements?

Accurate initial condition is prerequisite to an accurate prediction/forecast of near-surface meteorological elements and wind power. Although assimilating wind tower observations of wind farms in the initial condition has been proved feasible to improve the prediction, how and why assimilating wind tower observation improves initial condition are unclear. This study aims to improve the prediction by obtaining effective information from the initial conditions of assimilating wind tower observations. Results suggest that assimilating wind tower observations has local impacts on the initial condition, and the improvement of the initial condition is significantly related to the observed near-surface atmospheric stability. The forecast improvement by assimilating wind tower observations depends on two effective information: determining whether the initial condition is improved and the length of the improved initial condition can positively impact the forecast results. When the initial condition improves, forecast improvements can persist for about 12 h and are most distinct in the ultra-short term (0–4 h). Using the two effective information not only can distinctly improve the forecasts of near-surface wind, temperature, relative humidity, and wind power, but also can eliminate the stochastic forecasts. When the initial condition improves by assimilation, enhancing nudging intensity will have more positively impact on the prediction, and the root mean square error and mean absolute error in the ultra-short term can be reduced by 0.39 and 0.44 m s−1 for near-surface wind and 66.64 and 79.09 kW for wind power, respectively.