This study examines the seasonal and diurnal variations in wind speed in five major Iraqi cities: Mosul, Tikrit, Baghdad, Nasiriyah, and Basrah. It focuses on assessing wind energy potential and how well it aligns with peak electricity demand. From 2020 to 2024, hourly temperature and wind speed data were available from the NASA POWER database. Wind power density at a hub height of 100 meters was calculated using air density-corrected cubic wind speed. Descriptive statistics, one-way ANOVA, Pearson correlation, and the Peak Overlap Index (POI) were used to analyze temporal patterns and grid integration potential. The results indicate that all locations have Class 3 or higher wind resources during the summer, with mean wind speeds ranging from 4.85 m/s in Baghdad to 6.42 m/s in Tikrit. The NASA POWER database provided hourly wind speed and temperature data from 2020 to 2024. Air density-corrected cubic wind speed was used to calculate wind power density at 100 m hub height. Temporal patterns and grid integration potential were examined using descriptive statistics, one-way ANOVA, Pearson correlation, and the Peak Overlap Index (POI). With mean wind speeds ranging from 4.85 m/s in Baghdad to 6.42 m/s in Tikrit, the results show that all sites have Class 3 or higher wind resources during the summer. Wind power peaks sharply in the afternoon between 12:00 and 18:00, which is also when electricity consumption is at its highest. Strong temporal alignment is indicated by the Peak Overlap Index (POI), which is greater than 60% at all locations, with Tikrit and Mosul achieving 66.3%. Wind speed and temperature were found to be positively correlated (r = +0.41 to +0.56, p < 0.05). Although these results indicate that wind energy in Iraq is both plentiful and in line with demand, it is important to take into account the uncertainty in modeled data and assumptions (such as neutral stability). Prioritizing Tikrit and Mosul for utility-scale wind farm pilot projects is advised by the study, with additional validation through ground-based measurements.

This study examines the seasonal and diurnal variations in wind speed in five major Iraqi cities: Mosul, Tikrit, Baghdad, Nasiriyah, and Basrah. It focuses on assessing wind energy potential and how well it aligns with peak electricity demand. From 2020 to 2024, hourly temperature and wind speed data were available from the NASA POWER database. Wind power density at a hub height of 100 meters was calculated using air density-corrected cubic wind speed. Descriptive statistics, one-way ANOVA, Pearson correlation, and the Peak Overlap Index (POI) were used to analyze temporal patterns and grid integration potential. The results indicate that all locations have Class 3 or higher wind resources during the summer, with mean wind speeds ranging from 4.85 m/s in Baghdad to 6.42 m/s in Tikrit. The NASA POWER database provided hourly wind speed and temperature data from 2020 to 2024. Air density-corrected cubic wind speed was used to calculate wind power density at 100 m hub height. Temporal patterns and grid integration potential were examined using descriptive statistics, one-way ANOVA, Pearson correlation, and the Peak Overlap Index (POI). With mean wind speeds ranging from 4.85 m/s in Baghdad to 6.42 m/s in Tikrit, the results show that all sites have Class 3 or higher wind resources during the summer. Wind power peaks sharply in the afternoon between 12:00 and 18:00, which is also when electricity consumption is at its highest. Strong temporal alignment is indicated by the Peak Overlap Index (POI), which is greater than 60% at all locations, with Tikrit and Mosul achieving 66.3%. Wind speed and temperature were found to be positively correlated (r = +0.41 to +0.56, p < 0.05). Although these results indicate that wind energy in Iraq is both plentiful and in line with demand, it is important to take into account the uncertainty in modeled data and assumptions (such as neutral stability). Prioritizing Tikrit and Mosul for utility-scale wind farm pilot projects is advised by the study, with additional validation through ground-based measurements.