Integration of photovoltaics into power grids is difficult as solar energy is highly dependent on climate and geography; often fluctuating erratically. This causes penetrations and voltage surges, system instability, inefficient utilities planning and financial loss. Forecast models can help; however, time stamp, forecast horizon, input correlation analysis, data pre and post-processing, weather classification, network optimization, uncertainty quantification and performance evaluations need consideration. Thus, contemporary forecasting techniques are reviewed and evaluated. Input correlational analyses reveal that solar irradiance is most correlated with Photovoltaic output, and so, weather classification and cloud motion study are crucial. Moreover, the best data cleansing processes: normalization and wavelet transforms, and augmentation using generative adversarial network are recommended for network training and forecasting. Furthermore, optimization of inputs and network parameters, using genetic algorithm and particle swarm optimization, is emphasized. Next, established performance evaluation metrics MAE, RMSE and MAPE are discussed, with suggestions for including economic utility metrics. Subsequently, modelling approaches are critiqued, objectively compared and categorized into physical, statistical, artificial intelligence, ensemble and hybrid approaches. It is determined that ensembles of artificial neural networks are best for forecasting short term photovoltaic power forecast and online sequential extreme learning machine superb for adaptive networks; while Bootstrap technique optimum for estimating uncertainty. Additionally, convolutional neural network is found to excel in eliciting a model's deep underlying non-linear input-output relationships. The conclusions drawn impart fresh insights in photovoltaic power forecast initiatives, especially in the use of hybrid artificial neural networks and evolutionary algorithms.

由于太阳能高度依赖气候和地理环境，因此很难将光伏电池集成到电网中。经常波动不定。这会导致穿透和电压浪涌，系统不稳定，效用规划效率低下以及财务损失。预测模型可以提供帮助；但是，需要考虑时间戳，预测范围，输入相关性分析，数据前后处理，天气分类，网络优化，不确定性量化和性能评估。因此，对当代的预测技术进行了回顾和评估。输入相关分析表明，太阳辐照度与光伏输出最相关，因此，天气分类和云运动研究至关重要。此外，最佳的数据清理流程：归一化和小波变换，对于网络训练和预测，建议使用生成对抗网络进行扩充。此外，强调了使用遗传算法和粒子群算法优化输入和网络参数。接下来，讨论建立的绩效评估指标MAE，RMSE和MAPE，并提出包括经济效用指标的建议。随后，对建模方法进行了批判，客观比较并将其分类为物理，统计，人工智能，集成和混合方法。已确定，人工神经网络集成最适合预测短期光伏发电量预测，并且对于自适应网络而言在线连续极限学习机极佳；而Bootstrap技术最适合估计不确定性。另外，发现卷积神经网络在引发模型的深层非线性输入输出关系方面表现出色。得出的结论为光伏发电计划提供了新的见识，尤其是在使用混合人工神经网络和进化算法的过程中。