Abstract

Interpretable and scalable data-driven methodologies providing high granularity baseline predictions of energy use in buildings are essential for the accurate measurement and verification of energy renovation projects and have the potential of unlocking considerable investments in energy efficiency worldwide. Bayesian methodologies have been demonstrated to hold great potential for energy baseline modelling, by providing richer and more valuable information using intuitive mathematics. This paper proposes a Bayesian linear regression methodology for hourly baseline energy consumption predictions in commercial buildings. The methodology also enables a detailed characterization of the analyzed buildings through the detection of typical electricity usage profiles and the estimation of the weather dependence. The effects of different Bayesian model specifications were tested, including the use of different prior distributions, predictor variables, posterior estimation techniques, and the implementation of multilevel regression. The approach was tested on an open dataset containing two years of electricity meter readings at an hourly frequency for 1578 non-residential buildings. The best performing model specifications were identified, among the ones tested. The results show that the methodology developed is able to provide accurate high granularity baseline predictions, while also being intuitive and explainable. The building consumption characterization provides actionable information that can be used by energy managers to improve the performance of the analyzed facilities.

Abstract

Shasta Reservoir is the largest in California, formed by Shasta Dam on the Sacramento River, and plays a major role in the Central Valley Project (CVP) by providing water storage, flood control, hydroelectric power, and irrigation. This study employs advanced statistical methods to evaluate the reservoir’s reliability and operational risks using censored hydrological data. We propose an improved adaptive progressive censoring plan and apply established statistical techniques, maximum likelihood and maximum product of spacings, alongside Bayesian estimation. The Bayes estimates are obtained through the squared error loss function and based on two sources for the observed data, namely the likelihood and spacing functions. The focus is on estimating the distribution’s scale parameter and two critical reliability metrics: the reliability function and the hazard rate function. The approximate confidence intervals based on the two classical approaches of the scale parameter and reliability metrics are studied. The highest posterior density credible intervals are also discussed. A simulation study evaluates the model’s accuracy under diverse data scenarios, and its practical utility is demonstrated through real-world data from Shasta Reservoir. The problem of optimizing data collection strategies is discussed with the same real data. The findings underscore the model’s value in enhancing reservoir reliability assessments, offering actionable insights for hydrology, disaster preparedness, and sustainable resource management.OPEN ACCESS Received: 15/03/2025 Accepted: 12/06/2025 Published: 22/09/2025

Abstract

Abstract

Abstract

This study explores an advanced three-parameter generalized log-logistic (GLL) model by incorporating an innovative shape parameter into the conventional log-logistic framework, allowing for greater flexibility in modeling data with increasing, decreasing, and bathtub-shaped failure rates. The estimation challenge under adaptive progressively TypeII censored data is addressed through both classical (likelihood-based) and Bayesian inferential methods. Utilizing observed Fisher information, asymptotic confidence intervals for unknown parameters are derived, while a Markov chain in the Monte Carlo approach is employed to obtain Bayesian point estimates and the highest posterior density intervals. In Bayes’ setup, the GLL parameters are presumed to have independent gamma conjugate priors against various symmetric and asymmetric losses. To examine the accuracy of the acquired estimators, an extensive Monte Carlo simulation is used. Four real-life data sets from the physical and biomedical industries are analyzed to illustrate the feasibility of the proposed techniques in an actual-world scenario. Numerical analyses revealed that the suggested model outperforms the other five models in the literature, including the alpha-power exponential, exponentiated exponential, log-logistic, Weibull, and gamma distributions. The findings emphasize the effectiveness of the Bayesian Markov chain Monte Carlo approach over frequentist techniques, reinforcing its practical significance in reliability analysis and survival studies.OPEN ACCESS Received: 27/03/2025 Accepted: 08/05/2025 Published: 15/08/2025

Abstract

In this article, we study and introduce the Kavya-Manoharan power ChrisJerry distribution (KMPCJD) which is a new generation of the power Chris-Jerry distribution (PCJD) which is suitable for engineering and disability data. The probability density curves of KMPCJD demonstrate that it has practical applications in analyzing engineering and disability data in Saudi Arabia. Researchers have a lot of flexibility when developing statistical models for research on disability issues, since the hazard rate function (HRF) for KMPCJD can exhibit J-shaped, increasing, and decreasing trends. In addition, several significant KMPCJD features are calculated, including moments, reliability metrics, moment-generating function, and order statistics. Using data on engineering and disability difficulties, we estimate the parameters of KMPCJD and use classical and Bayesian techniques to assess their reliability and HRF under hybrid censored schemes. Asymptotic confidence/credible intervals are calculated. The numerical results show that when the sample size n increases while keeping other factors like r and T constant, the estimators for δ and λ show improved performance in terms of reduced Bias, mean square error (MSE), and narrower confidence intervals. Also, the Bayesian method also produces shorter credible intervals (LCCI) compared to the traditional confidence intervals (LACI) from ML and MPS methods, suggesting higher precision. To show the utility of the suggested distribution, it was tested in five datasets related to engineering and disability issues in Saudi Arabia. The KMPCJD performed better in terms of goodness of fit than a number of models, including the Kavya Manoharan Rayleigh inverted Weibull distribution, Kavya Manoharan Burr X distribution, exponentiated generalized power Lindley distribution, Weibull power Lindley distribution, power Lindley distribution, Kavya Manoharan generalized exponential distribution, power XLindley distribution, Kavya Manoharan unit exponentiated half logistic distribution, and PCJD. Due to its superior fit capabilities, the KMPCJD is suggested for data modeling in disciplines including engineering and disability difficulties.OPEN ACCESS Received: 27/08/2025 Accepted: 19/09/2025