solar_energy_12_cities_Amazon_Basin

Data from NASA Power Project, aiming the study of solar irradiance in the Amazon Basin, focusing 12 cities in the Amazonas State, Brazil. The data is daily basis, the target variable is the solar irradiance, and the input variables are the local temperature, local air humidity, local wind speed at 10m, local wind direction at 10m, percentage of the sky coverture, the total precipitation corrected. The time span covers 2017 to 2023. Deep learning has grown among the prediction tools used within renewable energy options. Solar energy belongs to the options with the lowest atmosphere impact after considering their limitations. In the last five years, Brazil has seen the expansion of wind and solar options almost all over the country, and to preserve the Amazon rainforest, the use of solar energy has helped large and small cities towards a greener future. The novelty of this research covers the use of Deep Learning with data from twelve cities in the state of Amazonas to forecast solar irradiation (W.h/m2) within 30 days. The data input came from ground stations, as much as possible, and NASA satellite models, with a daily time aggregation. The types of neural networks considered are Long Short-Term Memory (LSTM), a Multi-Layer Perceptron (MLP), and a LSTM Gated Recurrent Unit (GRU). Among the metrics used to check the algorithm´s performance, the Mean Absolute Percentage Error (MAPE) indicates that the values of this research are coherent with other scenarios to forecast solar energy; the boundary conditions were not the same, however. The lowest MAPE was observed in the city of Labrea with the LSTM GRU.

本数据集源自美国国家航空航天局（NASA）的电力项目，旨在研究亚马逊盆地的太阳辐照度，聚焦于巴西亚马孙州境内的12个城市。数据以日为基准，目标变量为太阳辐照度，输入变量包括当地气温、当地空气湿度、10米高处的当地风速、10米高处的当地风向、天空覆盖百分比以及校正后的总降水量。时间跨度涵盖2017年至2023年。在可再生能源的预测工具中，深度学习技术得到了广泛应用。在考虑其局限性后，太阳能属于对大气影响最低的能源选择之一。在过去五年中，巴西几乎全国范围内均见证了风能和太阳能的扩张，为了保护亚马逊雨林，太阳能的使用有助于大小城市迈向更加绿色的未来。本研究的创新之处在于利用亚马孙州十二个城市的深度学习数据预测未来30天内的太阳辐照度（W.h/m2）。数据输入主要来源于地面站，以及尽可能多的NASA卫星模型，并以日为时间聚合。所考虑的神经网络类型包括长短期记忆网络（LSTM）、多层感知器（MLP）以及LSTM门控循环单元（GRU）。在评估算法性能的指标中，平均绝对百分比误差（MAPE）表明，本研究的结果与其他预测太阳能的情景相符；然而，边界条件并不相同。在Labrea市观察到最低的MAPE值，使用的是LSTM GRU。