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01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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01:29Econometric Views (EViews)
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Econometric Views, often stylized as EViews, is a package that merges statistical analysis with econometric studies. It is designed to provide tools for time series analysis, forecasting, and econometric model simulation. The software originated from MicroTSP software and has evolved significantly since its inception in 1981. The history of EViews is marked by a continuous effort to enhance its computational speed and user interface. It was initially developed for large computing systems but...
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01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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01:17Design Example: Analyzing Capacity Contours for Flood Risk Assessment
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Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
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01:03Prediction Intervals
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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
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01:11Regression Analysis
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Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
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Data driven models on load forecasting: Case study Algeria.
Rania Farah1, Brahim Farou1, Zineddine Kouahla1
1Department of Computer Science, LabStic Laboratory, University 8 May 1945, Guelma, Algeria.
Data in Brief
|December 26, 2023
Summary
This study uses historical hourly energy consumption data from Algeria (2008-2020) to develop predictive models. These models aim to improve energy demand forecasting for better resource management.
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Area of Science:
- Data Science
- Energy Systems Analysis
- Statistical Modeling
Background:
- Accurate energy demand forecasting is crucial for resource management and societal needs.
- Historical data analysis is essential for developing reliable energy consumption models.
- Algerian energy consumption data from 2008-2020 provides a basis for this study.
Purpose of the Study:
- To develop and validate statistical, mathematical, and machine learning models for energy consumption forecasting.
- To analyze hourly energy consumption patterns using historical data.
- To enhance the accuracy of energy demand predictions.
Main Methods:
- Collection and analysis of hourly energy consumption data (2008-2020).
- Application of statistical techniques and machine learning principles.
- Development of predictive models based on historical consumption patterns.
Main Results:
- The study provides a comprehensive analysis of historical energy consumption data.
- Developed models offer valuable insights into energy usage patterns.
- The methodology enables accurate energy demand predictions.
Conclusions:
- Historical data analysis and advanced modeling are key to accurate energy forecasting.
- The developed models can significantly aid in energy resource planning and management.
- This research contributes to more efficient energy supply and demand balancing.