Statistical Model for Estimating Daily Solar Radiation for Renewable Energy Planning

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@article{1711332,
      author = {Olushola Damilare Odejobi, Kabir Sholagberu Ahmed},
      title = {Statistical Model for Estimating Daily Solar Radiation for Renewable Energy Planning},
      journal = {Iconic Research And Engineering Journals},
      year = {2018},
      volume = {2},
      number = {5},
      pages = {248-262},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1711332.pdf},
      abstract = {Accurate estimation of daily solar radiation is a critical requirement for renewable energy planning, particularly in the design, optimization, and forecasting of photovoltaic (PV) and solar thermal systems. Direct measurements of solar radiation, although precise, are often limited due to the high cost and uneven distribution of pyranometer networks, especially in developing regions. To address this challenge, statistical models have emerged as practical and cost-effective alternatives, leveraging meteorological and climatological parameters to predict daily global solar radiation with acceptable accuracy. The proposed statistical model integrates classical regression approaches with advanced time-series and hybrid machine learning methods to estimate daily solar radiation. Predictor variables such as sunshine duration, maximum and minimum temperatures, relative humidity, and cloud cover are incorporated, while satellite-based datasets serve to complement ground-based observations where station coverage is sparse. The model calibration process involves partitioning datasets into training and validation subsets, followed by cross-validation to enhance robustness and reduce overfitting. Performance evaluation is conducted using metrics such as root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and the coefficient of determination (R?), enabling comparative analysis across different modeling approaches. The applicability of the model extends to multiple dimensions of renewable energy planning, including solar PV system sizing, grid integration forecasting, and regional energy resource mapping. By providing reliable radiation estimates, the model supports more accurate energy yield predictions, reduces uncertainties in investment decisions, and enhances the operational efficiency of renewable energy infrastructures. Strategically, such modeling frameworks contribute to accelerating the global energy transition by improving planning capabilities, supporting climate-responsive policy frameworks, and fostering sustainable deployment of solar resources. Future work envisions the integration of big data analytics, Internet of Things (IoT) sensors, and artificial intelligence to achieve real-time, adaptive solar radiation forecasting.},
      keywords = {Statistical Modeling, Solar Radiation Estimation, Renewable Energy Planning, Solar Energy Forecasting, Time Series Analysis, Regression Models, Stochastic Modeling, Climate Data Analysis, Irradiance Measurement, Atmospheric Variables, Solar Resource Assessment, Weather Variability, Predictive Analytics},
      month = {November},
  }