Forecasting the energy generation from the solar power is considered challenging due to inaccuracies in forecasting, reliability issues and substantial economic losses in power systems. Hence, it is necessary to consider wide features from the solar power generation point of view. In this paper, the study uses large features set to feed the deep learning classifier for optimal prediction of energy generation from the photovoltaic (PV) plants. The features selection and prediction modules automates the process of optimal prediction of energy using Radial Belief Neural Network (RBNN). The Restricted Boltzmann Machines (RBM) is used for rule set generation based on the feature extracted and the rule set generation is powered by action-reward based Reinforcement Learning (RL) method. The experiments are conducted with rich set of input features on large PV plants that ranges between 1, 50, 100 and 1000. The performance of the proposed model is compared with various metrics that includes: Root mean squared error (RMSE), normalized root mean squared error (NRMSE), mean bias error (MBE), Mean absolute error (MAE), Maximum absolute error (MaxAE), mean absolute percentage error (MAPE), Kolmogorov–Smirnov test integral (KSI) and OVER metrics, Skewness and kurtosis and variability estimation metrics. The simulation results show that the RBNN offers improved prediction ability with reduced errors than other deep and machine learning classifiers.

由于预测不准确、可靠性问题和电力系统的重大经济损失，预测太阳能发电被认为具有挑战性。因此，有必要从太阳能发电的角度考虑广泛的特性。在本文中，该研究使用大特征集为深度学习分类器提供信息，以优化预测光伏 (PV) 发电厂的发电量。特征选择和预测模块使用径向置信神经网络 (RBNN) 自动执行最佳能量预测过程。受限玻尔兹曼机 (RBM) 用于基于提取的特征生成规则集，规则集生成由基于动作奖励的强化学习 (RL) 方法提供支持。实验是在大型光伏电站上使用丰富的输入特征集进行的，范围在 1、50、100 和 1000 之间。将所提出模型的性能与各种指标进行比较，包括：均方根误差 (RMSE)、归一化均值根平方误差 (NRMSE)、平均偏差误差 (MBE)、平均绝对误差 (MAE)、最大绝对误差 (MaxAE)、平均绝对百分比误差 (MAPE)、Kolmogorov–Smirnov 检验积分 (KSI) 和 OVER 指标、偏度和峰度和可变性估计指标。仿真结果表明，与其他深度学习和机器学习分类器相比，RBNN 提供了更好的预测能力，同时减少了错误。归一化均方根误差 (NRMSE)、平均偏差误差 (MBE)、平均绝对误差 (MAE)、最大绝对误差 (MaxAE)、平均绝对百分比误差 (MAPE)、Kolmogorov–Smirnov 检验积分 (KSI) 和 OVER 指标，偏度和峰度以及可变性估计指标。仿真结果表明，与其他深度学习和机器学习分类器相比，RBNN 提供了更好的预测能力，同时减少了错误。归一化均方根误差 (NRMSE)、平均偏差误差 (MBE)、平均绝对误差 (MAE)、最大绝对误差 (MaxAE)、平均绝对百分比误差 (MAPE)、Kolmogorov–Smirnov 检验积分 (KSI) 和 OVER 指标，偏度和峰度以及可变性估计指标。仿真结果表明，与其他深度学习和机器学习分类器相比，RBNN 提供了更好的预测能力，同时减少了错误。