Forecasting the industry’s electricity consumption is essential for energy planning in a given country or region. Thus, this study aims to apply time-series forecasting models (statistical approach and artificial neural network approach) industrial Brazilian system. For statistical approach, Holt–Winters, SARIMA, Dynamic Linear Model, TBATS (Trigonometric Box–Cox transform, ARMA errors, Trend, Seasonal components) were considered. of networks, NNAR (neural autoregression) MLP (multilayer...

Crude oil price forecasting is an important research area in the international bulk commodity market. However, as risk factors diversify, movements exhibit more complex nonlinear behavior. Hence, this study provides a comprehensive analysis of Brent crude prices by comparing various hybrid combinations linear and time series models. To end, first, logarithmic transformation used to stabilize variance series; second, original log decomposed into two new subseries, such long-run trend...

In today’s modern world, monthly forecasts of electricity consumption are vital in planning the generation and distribution energy utilities. However, properties these time series so complex that they difficult to model directly. Thus, this study provides a comprehensive analysis forecasting by comparing several decomposition techniques followed various models. To end, first, we decompose into three new subseries: long-term trend series, seasonal stochastic using different proposed methods....

Accurate and efficient demand forecasting is essential to grid stability, supply, management in today's electricity markets. Due the complex pattern of electric power time series, it challenging model them directly. Therefore, this research proposes a novel series ensemble approach forecast Peruvian market one month ahead. This treats first preprocessed for missing values, variance stabilization, normalization, stationarity, seasonality issues. Secondly, six single three their proposed...

<p>In today's electricity markets, accurate price forecasting provides valuable insights for decision-making among participants, ensuring reliable operation of the power system. However, complex characteristics time series hinder accessibility to forecasting. This study addressed this challenge by introducing a novel approach predicting prices in Peruvian market. involved preprocessing monthly addressing missing values, stabilizing variance, normalizing data, achieving stationarity,...

Abstract The prediction of air pollution is great importance in highly populated areas because it directly impacts both the management city’s economic activity and health its inhabitants. This work evaluates predicts Spatio-temporal behavior quality Metropolitan Lima, Peru, using artificial neural networks. conventional feedforward backpropagation known as Multilayer Perceptron (MLP) Recurrent Artificial Neural network Long Short-Term Memory networks (LSTM) were implemented for hourly...

Over the last 30 years, day-ahead electricity price forecasts have been critical to public and private decision-making. This importance has increased since global wave of deregulation liberalization in energy sector at end 1990s. Given these facts, this work presents a new decomposition–combination technique that employs several nonparametric regression methods various time-series models enhance accuracy efficiency forecasting. For purpose, first, original prices deals with treatment extreme...

In the present liberalized energy markets, electricity demand forecasting is critical for planning of generation capacity and required resources. An accurate efficient forecast can reduce risk power outages excessive generation. Avoiding blackouts crucial economic growth, an essential source industry. Considering these facts, this study presents a detailed analysis hourly by comparing novel decomposition methods with several univariate multivariate time series models. To that end, we use...

In the modern era, air pollution is one of most harmful environmental issues on local, regional, and global stages. Its negative impacts go far beyond ecosystems economy, harming human health sustainability. Given these facts, efficient accurate modeling forecasting for concentration ozone are vital. Thus, this study explores an in-depth analysis by comparing many hybrid combinations time series models. To end, in first phase, hourly decomposed into three new sub-series, including long-term...

<abstract><p>Traders and investors find predicting stock market values an intriguing subject to study in exchange markets. Accurate projections lead high financial revenues protect from risks. This research proposes a unique filtering-combination approach increase forecast accuracy. The first step is filter the original series of prices into two new series, consisting nonlinear trend long run stochastic component using Hodrick-Prescott filter. Next, all possible filtered...

Carbon dioxide (CO

The coronavirus pandemic has raised concerns about the emergence of other viral infections, such as monkeypox, which become a significant hazard to public health. Thus, this work proposes novel time series ensemble technique for analyzing and forecasting spread monkeypox in four highly infected countries with virus. This approach involved processing first cumulative confirmed case address variance stabilization, normalization, stationarity, nonlinear secular trend component. After that, five...

The main objective of this study is to model the concentration ozone in winter season on air quality through machine learning algorithms, detecting its impact population health. area involves four monitoring stations: Ate, San Borja, Santa Anita and Campo de Marte, all located Metropolitan Lima during years 2017, 2018 2019. Exploratory, correlational predictive approaches are presented. exploratory results showed that ATE station with highest prevalence pollution. Likewise, an hourly scale...

Air pollution due to air contamination by gases, liquids, and solid particles in suspension, is a great environmental public health concern nowadays. An important type of particulate matter with diameter 10 microns or less ([Formula: see text]) because one the determining factors that affect human size atmosphere degree permanence penetration they have respiratory system. Therefore, it extremely interesting monitor understand behavior [Formula: text] concentrations so do not exceed...

<abstract><p>The rise in global ozone levels over the last few decades has harmed human health. This problem exists several cities throughout South America due to dangerous of particulate matter air, particularly during winter season, making it a public health issue. Lima, Peru, is one ten with worst air pollution. Thus, efficient and precise modeling forecasting are critical for concentrations Lima. The focus on developing models anticipate concentrations, providing timely...

There is a great deficiency in the collection and disposal of solid waste, with considerable amount disposed dumps instead landfills. In this sense, objective research to propose waste mitigation plan through recovery District Santa Rosa, Ayacucho. For this, characterization was executed eight days, ANOVA it shown that there significant difference means between business pairs except bakery hotel. Through clustering, zones A B are highly correlated, reflecting organic greater than inorganic...

This study proposes a holistic maturity model to evaluate and optimize the performance of Peruvian universities. It addresses key dimensions such as favorable governance, university talent (including students, faculty, administrators), substantial resources, results. is based on Design Science Research methodology Mettler framework. On other hand, Delphi method was selected for its ability consolidate expert opinion. Aiken’s V coefficient used determine content validity, evaluating criteria...

Se diseñó un prototipo de boya multiparamétrica autónoma para abordar las limitaciones tecnológicas en el monitoreo la calidad del agua ambientes acuicultura. El objetivo fue desarrollar sistema modular y sustentable que integre energía fotovoltaica comunicación inalámbrica monitorear tiempo real parámetros críticos: pH, temperatura, oxígeno disuelto conductividad eléctrica. consta módulo emisor, receptor una plataforma transmisión datos a nube. Los materiales incluyeron PLA reforzado PETG,...

Population distribution function is a particular area in sample surveys, and several researchers have worked to improve the accuracy of this study by using auxiliary data. Recent studies estimate population applying stratified random sampling non-response techniques, but there are some limitations However, we study, which aims maximize estimating under combined effect groups. To achieve goal condition both introduce use variable two variables (mean ranks). We conduct various estimations for...

The academic success of university students is a problem that depends in multi-factorial way on the aspects related to student and career itself. A with this level complexity needs be faced integral approaches, which involves complement numerical quantitative analysis other types analysis. This study uses novel visual-predictive data approach obtain relevant information regarding performance from Peruvian university. joins together domain understanding data-visualization analysis,...

In today’s world, a country’s economy is one of the most crucial foundations. However, industries’ financial operations depend on their ability to meet electricity demands. Thus, forecasting consumption vital for properly planning and managing energy resources. this context, new approach based ensemble learning has been developed predict monthly consumption. The method divides time series into deterministic stochastic components. component, which consists secular long-term trend an annual...