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首页> 外文期刊>Energy sources >A kernel extreme learning machine-based neural network to forecast very short-term power output of an on-grid photovoltaic power plant
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A kernel extreme learning machine-based neural network to forecast very short-term power output of an on-grid photovoltaic power plant

机译：基于内核极端学习机的神经网络，用于预测栅极光伏发电厂的非常短期功率输出

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It is well-known that solar power depends on many complex parameters such as humidity, radiation, temperature, dust and wind speed. In order to cope with these complex structures and to provide an accurate forecast, the development of reliable and effective forecasting methodology is very significant. In this study, an Artificial Neural Network (ANN)-based system has been developed to forecast very short-term (2 to 4-h) power output of a grid tied Photovoltaic Power Plant (PVPP). An algorithm called Extreme Learning Machine (ELM) has gained increasing interest through its extremely fast learning and good generalization capability. The Kernel Extreme Learning Machine (KELM) which is the improved version of the ELM is proposed to develop a very short-term PVPP power forecast system. The most important feature of KELM is that it has less adjustable parameters and better generalization ability when compared with classical ELM. Experimental studies have been carried out on a grid tied PVPP that has 1 (MW) installed power capacity. The inputs of the KELM-based forecast system are selected as solar power, humidity, radiation and temperature. All data are divided into four parts to analyze the effect of seasons on the performance of the proposed forecast system. The comparison studies are carried out to clearly observe the forecast ability and performance of the KELM. From the experimental studies of the winter season which has not symmetric diurnal mean power curve, Correlation Coefficients (R) values of KELM are calculated as 0.896 for 2-h ahead, 0.865 for 3-h ahead, 0.833 for 4-h ahead. Those of ELM and (Levenberg Marquardt) LM are 0.866-0.812 for 2-h ahead, 0.831-0.669 for 3-h ahead, 0.799-0.502 for 4-h ahead, respectively. Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values of KELM for winter season are 12.08%-6.14% for 2-h ahead, 14.06%-7.43% for 3-h ahead, 15.33%-7.74% for 4-h ahead, respectively. While these values of ELM are obtained as 13.82%-7.05% for 2-h ahead, 15.52%-8.77% for 3-h ahead, 17.63%-9.33% for 4-h ahead. The RMSE and MAE values of LM are 17.90%-9.14% for 2-h ahead, 20.04%-9.85% for 3-h ahead, 21.22%-10.68% for 4-h ahead. According to the obtained results, it is clearly seen that KELM provides a more powerful and reliable forecasting performance.
机译：众所周知，太阳能功率取决于许多复杂的参数，如湿度，辐射，温度，灰尘和风速。为了应对这些复杂的结构并提供准确的预测，可靠和有效的预测方法的发展非常显着。在该研究中，已经开发了一种基于人工神经网络（ANN）的系统来预测网格捆扎光伏发电厂（PVPP）的非常短期（2至4-H）功率输出。一种称为极端学习机（ELM）的算法通过其极快的学习和良好的泛化能力获得了越来越兴趣。提出了作为ELM改进版本的内核极端学习机（KELM），以开发一个非常短期的PVPP电力预测系统。与古典榆树相比，KELM最重要的特征是它具有较差的参数和更好的泛化能力。在具有1（MW）安装的电力容量的网格绑定PVPP上进行了实验研究。基于Kelm的预测系统的输入被选为太阳能，湿度，辐射和温度。所有数据分为四个部分，以分析季节对提出的预测系统性能的影响。进行比较研究，以清楚地观察Kelm的预测能力和性能。从冬季冬季的实验研究没有对称的昼夜平均功率曲线，kelm的相关系数（R）值计算为0.896，为0.896，0.865，前方为0.833，未来4-H. ELM和（Levenberg Marquardt）LM的那些是0.866-0.812，适用于2-H，0.831-0.669为3-H，分别为0.799-0.502，分别为4-h。冬季KELM的根均方误差（RMSE）和平均绝对误差（MAE）值为2-H.2-H进入12.08％-6.14％，3-H未来为14.06％-7.43％，15.33％-7.74％为4 -h分别前进。虽然将这些ELM的值获得为13.82％-7.05％的2-H，前进为3-H.52％-8.77％，未来为4-H.63％-9.33％。 LM的RMSE和MAE值为17.90％-9.14％，适用于2-H，前进为3-H.2.04％-9.85％，未来4-H.22％-10.68％。根据获得的结果，清楚地看出，Kelm提供了更强大且可靠的预测性能。

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