Short-term PV Output Power Forecasting Based on CEEMDAN-AE-GRU

2022 ◽  
Author(s):  
Na Zhang ◽  
Qiang Ren ◽  
Guangchen Liu ◽  
Liping Guo ◽  
Jingyu Li
Keyword(s):  
Output Power ◽  
Short Term ◽  
Power Forecasting

Application of extreme learning machine for short term output power forecasting of three grid-connected PV systems

2017 ◽  
Vol 167 ◽  
pp. 395-405 ◽  
Author(s):  
Monowar Hossain ◽  
Saad Mekhilef ◽  
Malihe Danesh ◽  
Lanre Olatomiwa ◽  
Shahaboddin Shamshirband
Keyword(s):  
Extreme Learning Machine ◽  
Output Power ◽  
Short Term ◽  
Pv Systems ◽  
Learning Machine ◽  
Power Forecasting

Short-Term Power Forecasting of Small-Scale Hydropower Based on Projection Pursuit Algorithm

2013 ◽  
Vol 336-338 ◽  
pp. 764-769
Author(s):  
Wen Xia Liu ◽  
Xi Zhou ◽  
Xiao Bo Xu ◽  
Mei Mei Xu
Keyword(s):  
Output Power ◽  
Load Forecasting ◽  
Projection Pursuit ◽  
Forecast Accuracy ◽  
Small Scale ◽  
Forecasting Model ◽  
Practical Application ◽  
Short Term ◽  
Forecasting Method ◽  
Power Forecasting

Compared with other traditional energy, the small-scale hydropower which is intermittent energy can not be stored and scheduled. The greater fluctuant of the output power of small-scale hydropower leads to great difficult to the operation of the power system. Most of the existing small-scale hydropower forecasting is considered as the load forecasting factors, and there is not effective forecasting method. This paper establishes an output power forecasting model of the small-scale hydropower based on Projection Pursuit. The simulation results show that the new algorithm has a strong practical application in the small-scale hydropower output power forecasting and the forecast accuracy meets the scheduling requirements.

scholarly journals A Power-Forecasting Method for Geographically Distributed PV Power Systems using Their Previous Datasets

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4815 ◽  
Author(s):  
Yosui Miyazaki ◽  
Yusuke Kameda ◽  
Junji Kondoh
Keyword(s):  
Power Systems ◽  
Output Power ◽  
Power Output ◽  
Percentage Error ◽  
Support Vector ◽  
Short Term ◽  
Pv System ◽  
Forecasting Method ◽  
Geographically Distributed ◽  
Power Forecasting

The number of photovoltaic (PV) power systems being installed worldwide has been increasing. This has resulted in maintenance of an adequate balance between demand and supply becoming a great concern for power system operators. Forecasting PV power outputs is a promising countermeasure that has been garnering significant interest. Conventional methods for achieving this often use learning methods, such as neural networks and support vector regression. In contrast, this paper proposes a short-term power-forecasting method for geographically distributed PV systems that uses only their previous output power data. In the proposed method, first, the ratio of the power generation output to the maximum power output value for each observation instance in a designated period for each PV system at a certain date and time is obtained. Then, the future geographical distribution of the ratio is predicted from the temporal change (motion) of the preceding distribution. Finally, the predicted ratio is reconverted into the power output to perform short-term power forecasting. The results of total PV output power prediction in the Kanto area of Japan indicate that the proposed method has an average mean absolute percentage error of 4.23% and root mean square error of 0.69 kW, which verifies its efficacy.

scholarly journals Short-term output power forecasting of photovoltaic systems based on the deep belief net

2017 ◽  
Vol 9 (9) ◽  
pp. 168781401771598 ◽  
Author(s):  
Ling-Ling Li ◽  
Peng Cheng ◽  
Hsiung-Cheng Lin ◽  
Hao Dong
Keyword(s):  
Output Power ◽  
Photovoltaic Systems ◽  
Short Term ◽  
Deep Belief Net ◽  
Power Forecasting
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