Currently, in most countries, the electricity sector is liberalized, and traded deregulated markets. In these markets, demand determined day before physical delivery through (semi-)hourly concurrent auctions. Hence, accurate forecasts are essential for efficient effective management of power systems. The prices, however, exhibit specific features, including non-constant mean variance, calendar effects, multiple periodicities, high volatility, jumps, so on, which complicate forecasting...

Efficient modeling and forecasting of electricity prices are essential in today’s competitive markets. However, price is not easy due to the specific features series. This study examines performance an ensemble-based technique for short-term spot Italian market (IPEX). To this end, time series divided into deterministic stochastic components. The component that includes long-term trends, annual weekly seasonality, bank holidays, estimated using semi-parametric techniques. On other hand,...

In recent years, efficient modeling and forecasting of electricity prices became highly important for all the market participants developing bidding strategies making investment decisions. However, as exhibit specific features, such periods high volatility, seasonal patterns, calendar effects, nonlinearity, etc., their accurate is challenging. This study proposes a functional method prices. A autoregressive model order P suggested short-term price in markets. The applicability improved with...

Electricity demand and price forecasting are key components for the market participants system operators as precise forecasts necessary to manage power systems effectively. However, electricity prices challenging due their specific features, such high frequency, volatility, long trend, nonconstant mean variance, reversion, multiple seasonalities, calendar effects, spikes/jumps. Thus, main aim of this study is propose models that can efficiently forecast prices. To end, time series...

In today’s liberalized electricity markets, modeling and forecasting demand data are highly important for the effective management of power system. However, is a challenging task due to specific features it exhibits. These include presence extreme values, spikes or jumps, multiple periodicities, long trend, bank holiday effect. addition, forecasts required complete day as decided before physical delivery. Therefore, this study aimed investigate performance models based on functional...

The increasing shortage of electricity in Pakistan disturbs almost all sectors its economy. As, for accurate policy formulation, precise and efficient forecasts consumption are vital, this paper implements a forecasting procedure based on components estimation technique to forecast medium-term consumption. To end, the series is divided into two major components: deterministic stochastic. For component, we use parametric nonparametric models. stochastic component modeled by using four...

Over the last three decades, accurate modeling and forecasting of electricity prices has become a key issue in competitive markets. As price series usually exhibit several complex features, such as high volatility, seasonality, calendar effect, non-stationarity, non-linearity mean reversion, is not trivial task. However, participants market need forecast to make decisions their daily activity market, trading, risk management or future planning. In this study we consider linear nonlinear...

Nowadays, modeling and forecasting electricity spot prices are challenging due to their specific features, including multiple seasonalities, calendar effects, extreme values (also known as jumps, spikes, or outliers). This study aims provide a comprehensive analysis of price by comparing several outlier filtering techniques followed various frameworks. To this end, first treated with five different then replaced four replacement approaches. Next, the spikes-free series is divided into...

Nowadays, short-term traffic flow forecasting has gained increasing attention from researchers due to congestion in many large and medium-sized cities that pose a serious threat sustainable urban development. To this end, research examines the performance of functional time series modeling forecast ultra-short term. An appealing feature approach is unlike other methods, it provides information over whole day, thus, forecasts can be obtained for any within day. Within approach, Functional...

This study addressed the complex challenges associated with landslide detection along Karakoram Highway (KKH), where tectonic events and data availability limitations posed significant obstacles. To overcome these hurdles, research framework encompassed several critical components. First, it tackled issue of multicollinearity through application statistical measures such as Variable Inflation Factor (VIF) Information Gain (IG). Secondly, emphasized importance selecting a area that would...

It is well known that different socioeconomic, household sizes, school levels, and cultural factors are highly related to the problem of out-of-school children. The main objective this study quantify determinants children in Pakistan. For study, data used from Pakistan Social Living Standards Measurement (PSLM) survey, collected for year 2015–16. aged 6–16 years who or out considered as a unit analysis. Unlike previous studies generally provide theoretical framework empirical investigation...

ABSTRACT This study develops three surveillance schemes to monitor a two‐step cascade manufacturing operation with reliability data. The designed schemes, namely, lower‐sided cumulative sum (CUSUM), exponentially weighted moving average (EWMA) and without reflecting barrier, are based on the proportional odd model account for dependent structure of operations. reliability‐based quality variable corresponds second step is assumed follow discrete Weibull distribution. efficiency proposed...

Abstract This work proposes a new approach for the prediction of electricity price based on forecasting aggregated purchase and sale curves. The basic idea is to model hourly curves, predict them find intersection predicted curves in order obtain equilibrium market volume. Modeling performed by means functional data analysis methods. More specifically, parametric (FAR) nonparametric (NPFAR) autoregressive models are considered compared some benchmarks. An appealing feature that, unlike other...

An important issue in competitive energy markets is the accurate and efficient wind speed forecasting for power production. However, models developed one location usually do not match other site various reasons like changes terrain, different patterns, atmospheric factors such as temperature, pressure, humidity, etc. Thus, introducing a flexible model that captures all features challenging task. This paper proposes functional data analysis (FDA) approach to forecast variant daily profiles...

A precise forecast of atmospheric temperatures is essential for various applications such as agriculture, energy, public health, and transportation. Modern advancements in technology have led to the development sensors other tools collect high-frequency air temperature data. However, accurate forecasts are challenging due their specific features including high dimensionality, non-linearity, seasonal dependency, etc. To address these forecasting challenges, this study proposes a functional...

Ridge regression is used to circumvent the problem of multicollinearity among predictors and many estimators for ridge parameter k are available in literature. However, if level collinearity high, existing also have high mean square errors (MSE). In this paper, we consider some propose new estimation k. Extensive Monte Carlo simulations as well a real-life example evaluate performance proposed based on MSE criterion. The results show superiority our compared estimators.

Electronic devices are integral part of our life and modeling their lifetime is the most challenging interesting field in reliability analysis. To investigate failure behavior electronic analysis commonly used. In literature, however, it reported that one five device a result corrosion to save electricity predict future failures, important summarize data by some flexible probability models. This will not only help companies, but also users providing them information about maximum voltage...

The present communication develops the tools for Bayesian prediction of Gompertz distribution based on CSPALT. Metropolis-Hastings algorithm is applied to evaluate BPIs a censored sample unified hybrid censoring scheme. In order investigate impact methodologies adopted, two numerical examples are performed. simulated results show that reducing percentages causes smaller BPIs. flexibility UHCS in evaluating can be helped overcome many difficulties engineering problems.

Regression analysis is a statistical process that utilizes two or more predictor variables to predict response variable. When the predictors included in regression model are strongly correlated with each other, problem of multicollinearity arises model. Due this problem, variance increases significantly, leading inconsistent ordinary least-squares estimators may lead invalid inferences. There numerous existing strategies used solve issue, and one most methods ridge regression. The aim work...

Control charts play a vital role in process monitoring to ensure the product's desired standards. Due rapid improvements data collection methods, multivariate are preferred over univariate charts. This paper proposes bivariate exponentially weighted moving average chart for simultaneous of mean vector Gumbel's Pareto type II (also known as Lomax distribution) time-between-events data. The performance proposed is assessed through run length, median and standard deviation length criteria. To...

Air pollution, especially ground-level ozone, poses severe threats to human health and ecosystems. Accurate forecasting of ozone concentrations is essential for reducing its adverse effects. This study aims use the functional time series approach model concentrations, a method less explored in literature, compare it with traditional machine learning models. To this end, concentration hourly first filtered yearly seasonality using smoothing splines that lead us stochastic (residual)...

This work considers the issue of modeling and forecasting electricity prices within functional time series approach. As this is often performed by estimating predicting different components price dynamics, we study whether jointly components, able to account for their inter-relations, could improve prediction with respect a separate instance modeling. To investigate issue, consider compare predictive performance four predictors. The first two, namely Smoothing Splines-Seasonal Autoregressive...

Efficient modeling and forecasting for the electricity demand is an important issue in competitive market. In most markets daily determined day before delivery by means of (semi-)hourly auctions following day. Therefore, adequate reliable day-ahead forecasts are very important. this paper, performances parametric non models based on functional approach compared with those other standard models, namely univariate AR kernel-based nonparametric multivariate models. Empirical results refer to...