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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.

|December 26, 2023

Summary

This summary is machine-generated.

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.

Keywords:

Accurate predictions Electricity Energy Machine learning Statistical techniques

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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.