This paper reviews concentrated solar power (CSP) technologies and presents a methodology for predicting hourly beam irradiation from monthly average data. CSP plants, such as parabolic trough collectors (PTC) and solar power towers (SPT), are gaining interest due to their potential for renewable energy generation. However, the variability of solar irradiation poses challenges, particularly on cloudy days and at night, where thermal energy storage (TES) and backup systems (BS) are needed to ensure continuous operation. Accurate estimation of daily solar irradiation is crucial for optimal CSP design, as monthly data are often insufficient for precise predictions. The paper outlines a method to convert monthly data into hourly beam irradiation using existing literature and meteorological data. It also illustrates predictions for different SPT locations and discusses the application of these predictions in simulating plant configurations. The methodology and results demonstrate the potential of CSP technologies, particularly SPT, in providing stable energy supply. The paper also compares different CSP technologies, highlighting the advantages of SPT in terms of efficiency, lower operating costs, and scalability. It discusses the importance of TES and BS in enhancing CSP performance and provides a framework for calculating global and diffuse solar irradiation. The study concludes that SPT has significant potential for widespread application, especially in regions with high direct normal irradiance (DNI). The paper also presents results for selected locations, showing the variability of solar irradiation throughout the year and the impact of TES and BS on plant performance. The methodology is validated using data from various locations, demonstrating its effectiveness in predicting solar irradiation and supporting CSP design. The study emphasizes the importance of accurate solar irradiation data in optimizing CSP operations and highlights the role of TES and BS in ensuring reliable energy supply.This paper reviews concentrated solar power (CSP) technologies and presents a methodology for predicting hourly beam irradiation from monthly average data. CSP plants, such as parabolic trough collectors (PTC) and solar power towers (SPT), are gaining interest due to their potential for renewable energy generation. However, the variability of solar irradiation poses challenges, particularly on cloudy days and at night, where thermal energy storage (TES) and backup systems (BS) are needed to ensure continuous operation. Accurate estimation of daily solar irradiation is crucial for optimal CSP design, as monthly data are often insufficient for precise predictions. The paper outlines a method to convert monthly data into hourly beam irradiation using existing literature and meteorological data. It also illustrates predictions for different SPT locations and discusses the application of these predictions in simulating plant configurations. The methodology and results demonstrate the potential of CSP technologies, particularly SPT, in providing stable energy supply. The paper also compares different CSP technologies, highlighting the advantages of SPT in terms of efficiency, lower operating costs, and scalability. It discusses the importance of TES and BS in enhancing CSP performance and provides a framework for calculating global and diffuse solar irradiation. The study concludes that SPT has significant potential for widespread application, especially in regions with high direct normal irradiance (DNI). The paper also presents results for selected locations, showing the variability of solar irradiation throughout the year and the impact of TES and BS on plant performance. The methodology is validated using data from various locations, demonstrating its effectiveness in predicting solar irradiation and supporting CSP design. The study emphasizes the importance of accurate solar irradiation data in optimizing CSP operations and highlights the role of TES and BS in ensuring reliable energy supply.