Addressing energy challenges in Iraq: Forecasting power supply and demand using artificial intelligence models.

Area of Science:

• Energy Systems Analysis
• Computational Intelligence
• Economic Forecasting

Background:

• Global energy demand is rising due to technological advancements and population growth.
• Developing nations, like Iraq, face challenges where energy demand outstrips generation capacity.
• Effective electricity supply and demand management is critical for national stability and economic progress.

Purpose of the Study:

• To evaluate advanced forecasting models for electricity supply and demand in Iraq.
• To analyze model performance using a novel dataset from the Iraqi Ministry of Electricity (2019-2021).
• To provide data-driven insights for improving Iraq's energy security and resource allocation.

Main Methods:

• Utilized a dataset from the Iraqi Ministry of Electricity spanning 2019-2021.
• Employed diverse forecasting models: Linear Regression, XGBoost, Random Forest, LSTM, TCN, and MLP.
• Evaluated model performance across multiple forecast horizons (24, 48, 72, and 168 hours).

Main Results:

• Linear Regression consistently outperformed other models for electricity demand forecasting.
• XGBoost demonstrated superior performance in electricity supply forecasting.
• Statistical analysis confirmed performance differences among models, with no significant pairwise differences for supply forecasting.

Conclusions:

• Accurate energy forecasting is vital for Iraq's energy security, resource management, and policy formulation.
• The study offers tools to mitigate power shortages and foster economic development.
• Recommendations include adopting innovative methods, incorporating external data, and developing region-specific models for Iraq.