Energy is the primary driving force in improvement of the human life cycle. All the activities for the betterment of human life are dependent on some form of energy. Conventional energy sources rely on fossil fuels which have limited reserves and we are bound to exhaust them soon. On the other hand, non-conventional/renewable energy sources are produced on a regular basis and are clean without any polluting emissions. These sources include solar, wind, hydraulic, biomass/bio gas, geothermal, tidal, etc. Solar energy is one of the primary sources in countries like India, but it does have drawbacks like high initial cost, dependency on weather, expensive storage, space requirement, etc. It is therefore imperative to create accurate solar radiation forecasting models to identify and address these issues. Forecasting models are created based on daily or hourly data and are location specific. In this work, binning based machine learning models are proposed for accurately forecasting hourly solar radiation. These models are data driven clustering based models. The clusters are identified based on geographic locations. The proposed approach also helps reduce the number of required models without compromising the high accuracy. In this work, global and diffuse solar radiation data, gathered from five geographically distinct stations from India, is analyzed. Validation of these models demonstrate increased performance. The number models required are also significantly smaller compared to the daily or hourly models.

能源是改善人类生命周期的主要驱动力。所有改善人类生活的活动都依赖于某种形式的能源。传统能源依赖于化石燃料，而化石燃料的储量有限，很快就会耗尽。另一方面，非常规/可再生能源是定期生产的，并且是清洁的，没有任何污染排放。这些来源包括太阳能、风能、水力、生物质/沼气、地热能、潮汐能等。太阳能是印度等国家的主要来源之一，但它确实存在初始成本高、依赖天气、存储成本昂贵等缺点。因此，建立准确的太阳辐射预测模型来识别和解决这些问题势在必行。预测模型是根据每日或每小时的数据创建的，并且是特定于位置的。在这项工作中，提出了基于分箱的机器学习模型来准确预测每小时的太阳辐射。这些模型是基于数据驱动的聚类模型。集群是根据地理位置来识别的。所提出的方法还有助于减少所需模型的数量，而不会影响高精度。在这项工作中，分析了从印度五个不同地理位置的站点收集的全球和漫射太阳辐射数据。这些模型的验证证明了性能的提高。与每日或每小时模型相比，所需的模型数量也明显减少。