Comparison of short-term solar irradiance forecasting methods when weather conditions are complicated

2018 ◽  
Vol 10 (5) ◽  
pp. 053501 ◽  
Author(s):  
Yunjun Yu ◽  
Junfei Cao ◽  
Xiaofeng Wan ◽  
Fanpeng Zeng ◽  
Jianbo Xin ◽  
...  
Keyword(s):  
Solar Irradiance ◽  
Weather Conditions ◽  
Short Term ◽  
Forecasting Methods

scholarly journals Study on Very Short-term Solar Irradiance Forecasting Methods using Ground based Cloud Image

2012 ◽  
Vol 2 ◽  
pp. 174-179 ◽  
Author(s):  
Cao Xiao ◽  
Chen Zhi-bao ◽  
Ding Yuyu ◽  
Zhou Hai ◽  
Ding Jie
Keyword(s):  
Solar Irradiance ◽  
Short Term ◽  
Forecasting Methods

scholarly journals An LSTM Short-Term Solar Irradiance Forecasting Under Complicated Weather Conditions

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 145651-145666 ◽  
Author(s):  
Yunjun Yu ◽  
Junfei Cao ◽  
Jianyong Zhu
Keyword(s):  
Solar Irradiance ◽  
Weather Conditions ◽  
Short Term

scholarly journals Short-term load forecasting using Theta method

2019 ◽  
Vol 84 ◽  
pp. 01004 ◽  
Author(s):  
Grzegorz Dudek
Keyword(s):  
Empirical Studies ◽  
Load Forecasting ◽  
Seasonal Cycles ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Forecasting Methods ◽  
Short Term Load Forecasting ◽  
Data Definition ◽  
System Data ◽  
Theta Method

The Theta method attracted the attention of researchers and practitioners in recent years due to its simplicity and superior forecasting accuracy. Its performance has been confirmed by many empirical studies as well as forecasting competitions. In this article the Theta method is tested in short-term load forecasting problem. The load time series expressing multiple seasonal cycles is decomposed in different ways to simplify the forecasting problem. Four variants of input data definition are considered. The standard Theta method is uses as well as the dynamic optimised Theta model proposed recently. The performances of the Theta models are demonstrated through an empirical application using real power system data and compared with other popular forecasting methods.

scholarly journals Performance Evaluation of Neural Network-Based Short-Term Solar Irradiation Forecasts

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3030
Author(s):  
Simon Liebermann ◽  
Jung-Sup Um ◽  
YoungSeok Hwang ◽  
Stephan Schlüter
Keyword(s):  
Neural Network ◽  
Goodness Of Fit ◽  
Renewable Energy Sources ◽  
Weather Conditions ◽  
Solar Irradiation ◽  
Weather Data ◽  
Short Term ◽  
Climate Conditions ◽  
Lstm Network ◽  
The Time Domain

Due to the globally increasing share of renewable energy sources like wind and solar power, precise forecasts for weather data are becoming more and more important. To compute such forecasts numerous authors apply neural networks (NN), whereby models became ever more complex recently. Using solar irradiation as an example, we verify if this additional complexity is required in terms of forecasting precision. Different NN models, namely the long-short term (LSTM) neural network, a convolutional neural network (CNN), and combinations of both are benchmarked against each other. The naive forecast is included as a baseline. Various locations across Europe are tested to analyze the models’ performance under different climate conditions. Forecasts up to 24 h in advance are generated and compared using different goodness of fit (GoF) measures. Besides, errors are analyzed in the time domain. As expected, the error of all models increases with rising forecasting horizon. Over all test stations it shows that combining an LSTM network with a CNN yields the best performance. However, regarding the chosen GoF measures, differences to the alternative approaches are fairly small. The hybrid model’s advantage lies not in the improved GoF but in its versatility: contrary to an LSTM or a CNN, it produces good results under all tested weather conditions.

A Summary of Short-Term Traffic Flow Forecasting Methods

ICCTP 2011 ◽  
2011 ◽  
Author(s):  
Gang Chang ◽  
Yi Zhang ◽  
Danya Yao ◽  
Yun Yue
Keyword(s):  
Traffic Flow ◽  
Short Term ◽  
Traffic Flow Forecasting ◽  
Forecasting Methods

scholarly journals Stellar activity, differential rotation, and exoplanets

2010 ◽  
Vol 6 (S273) ◽  
pp. 89-95 ◽  
Author(s):  
A. F. Lanza
Keyword(s):  
Time Series ◽  
Differential Rotation ◽  
Solar Irradiance ◽  
Giant Planet ◽  
Total Solar Irradiance ◽  
Short Term ◽  
Stellar Activity ◽  
Transiting Planets ◽  
Spot Model ◽  
Spot Activity

AbstractThe photospheric spot activity of some of the stars with transiting planets discovered by the CoRoT space experiment is reviewed. Their out-of-transit light modulations are fitted by a spot model previously tested with the total solar irradiance variations. This approach allows us to study the longitude distribution of the spotted area and its variations versus time during the five months of a typical CoRoT time series. The migration of the spots in longitude provides a lower limit for the surface differential rotation, while the variation of the total spotted area can be used to search for short-term cycles akin the solar Rieger cycles. The possible impact of a close-in giant planet on stellar activity is also discussed.

Short-term scheduling problem in smart grid considering reliability improvement in bad weather conditions

2017 ◽  
Vol 11 (10) ◽  
pp. 2521-2533 ◽  
Author(s):  
Babak Yousefi-khangah ◽  
Saeid Ghassemzadeh ◽  
Seyed Hossein Hosseini ◽  
Behnam Mohammadi-Ivatloo
Keyword(s):  
Smart Grid ◽  
Weather Conditions ◽  
Scheduling Problem ◽  
Short Term ◽  
Reliability Improvement ◽  
Bad Weather

A comparative study of short-term load forecasting methods in distribution network

2021 ◽  
Vol 15 (1) ◽  
pp. 23-35
Author(s):  
Tuan Ho Le ◽  
◽  
Quang Hung Le ◽  
Thanh Hoang Phan
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Arima Model ◽  
Distribution Network ◽  
Load Forecasting ◽  
Short Term ◽  
Load Demand ◽  
Forecasting Methods ◽  
Short Term Load Forecasting

Short-term load forecasting plays an important role in building operation strategies and ensuring reliability of any electric power system. Generally, short-term load forecasting methods can be classified into three main categories: statistical approaches, artificial intelligence based-approaches and hybrid approaches. Each method has its own advantages and shortcomings. Therefore, the primary objective of this paper is to investigate the effectiveness of ARIMA model (e.g., statistical method) and artificial neural network (e.g., artificial intelligence based-method) in short-term load forecasting of distribution network. Firstly, the short-term load demand of Quy Nhon distribution network and short-term load demand of Phu Cat distribution network are analyzed. Secondly, the ARIMA model is applied to predict the load demand of two distribution networks. Thirdly, the artificial neural network is utilized to estimate the load demand of these networks. Finally, the estimated results from two applied methods are conducted for comparative purposes.

The role of short-term weather conditions in temporal dynamics of fire regime features in mainland Spain

2019 ◽  
Vol 241 ◽  
pp. 575-586 ◽  
Author(s):  
Adrián Jiménez-Ruano ◽  
Marcos Rodrigues Mimbrero ◽  
W. Matt Jolly ◽  
Juan de la Riva Fernández
Keyword(s):  
Temporal Dynamics ◽  
Fire Regime ◽  
Weather Conditions ◽  
Short Term
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