Wind and solar energy are two popular forms of renewable energy used in microgrids and facilitating the transition towards net-zero carbon emissions by 2050.However,they are exceedingly unpredictable since they rely h...Wind and solar energy are two popular forms of renewable energy used in microgrids and facilitating the transition towards net-zero carbon emissions by 2050.However,they are exceedingly unpredictable since they rely highly on weather and atmospheric conditions.In microgrids,smart energy management systems,such as integrated demand response programs,are permanently established on a step-ahead basis,which means that accu-rate forecasting of wind speed and solar irradiance intervals is becoming increasingly crucial to the optimal operation and planning of microgrids.With this in mind,a novel“bidirectional long short-term memory network”(Bi-LSTM)-based,deep stacked,sequence-to-sequence autoencoder(S2SAE)forecasting model for predicting short-term solar irradiation and wind speed was developed and evaluated in MATLAB.To create a deep stacked S2SAE prediction model,a deep Bi-LSTM-based encoder and decoder are stacked on top of one another to reduce the dimension of the input sequence,extract its features,and then reconstruct it to produce the forecasts.Hyperparameters of the proposed deep stacked S2SAE forecasting model were optimized using the Bayesian optimization algorithm.Moreover,the forecasting performance of the proposed Bi-LSTM-based deep stacked S2SAE model was compared to three other deep,and shallow stacked S2SAEs,i.e.,the LSTM-based deep stacked S2SAE model,gated recurrent unit-based deep stacked S2SAE model,and Bi-LSTM-based shallow stacked S2SAE model.All these models were also optimized and modeled in MATLAB.The results simulated based on actual data confirmed that the proposed model outperformed the alternatives by achieving an accuracy of up to 99.7%,which evidenced the high reliability of the proposed forecasting.展开更多
Wind-solar hybrid systems are employed extensively due to certain advantages. However, two problems exist in their application: the PV modules operate at high temperatures, particularly during summer, and low wind pow...Wind-solar hybrid systems are employed extensively due to certain advantages. However, two problems exist in their application: the PV modules operate at high temperatures, particularly during summer, and low wind power cannot be utilized. To solve these two problems, a novel hybrid system is designed based on PV/thermal systems, in which PV modules are cooled with fans driven by a wind turbine. This paper studies the practicability of the novel hybrid system. First, the electrical performance of the wind turbine is compared using a fan and battery load,respectively. Second, different types and numbers of fans are tested to obtain the largest air volume. Third, the height of the air duct on the back of the PV module is optimized and the cooling effect is studied. Results show that a 24 V DC fan is more appropriate for the novel system than a 12 V DC fan, as it provides a greater air volume, and with a switch wind speed of 3.0 m/s the power of PV module shows a maximum increase of 8.0%.展开更多
Computational fluid dynamics( CFD) techniques are used to investigate effects of both wind direction and wind speed on net solar heat gain of south wall with internal insulation in winter.Results show that wind effect...Computational fluid dynamics( CFD) techniques are used to investigate effects of both wind direction and wind speed on net solar heat gain of south wall with internal insulation in winter.Results show that wind effect has a significant influence on the net solar heat gain,where the impact of wind direction is stronger than that of wind speed. For regions in lower reaches of the Yangtze River,difference of their average net solar heat gains( NSHGS) is about 20% due to various wind speeds and wind directions.Buildings in districts with a dominant wind direction of north achieve the highest solar energy utilization.展开更多
The investment levels in electricity production capacity from variable Renewable Energy Sources have substantially grown in Brazil over the last decades,following the worldwide-seeking-goal of a carbon-neutral economy...The investment levels in electricity production capacity from variable Renewable Energy Sources have substantially grown in Brazil over the last decades,following the worldwide-seeking-goal of a carbon-neutral economy and the country’s incentives in diversifying its generation mix.From a long-term perspective,the current non-storable capability of renewable energy sources requires an adequate uncertainty characterization over the years.In this context,the main objective of this work is to provide a thorough descriptive analytics of the time-linked hourly-based daily dynamics of wind speed and solar irradiance in the main resourceful regions of Brazil.Leveraging on unsupervised Machine Learning methods,we focus on identifying similar days over the years,Representative Days,that can depict the fundamental underlying behaviour of each source.The analysis is based on a historical dataset of different sites with the highest potential and installed capacity of each source spread over the country:three in the Northeast and one in the South Regions,for wind speed;and three in the Northeast and one in the Southeast Regions,for solar irradiance.We use two Partitioning Methods(𝐾-Means and𝐾-Medoids),the Hierarchical Ward’s Method,and a Model-Based Method(Self-Organizing Maps).We identified that wind speed and solar irradiance can be effectively represented,respectively,by only two representative days,and two or three days,depending on the region and method(segments data with respect to the intensity of each source).Analysis with higher Representative Days highlighted important hidden patterns such as different wind speed modulations and solar irradiance peak-hours along the days.展开更多
The space particle component detector on Fengyun-1 satellite which works at the sun-synchronous orbit of about 870 km altitude has detected relativistic electrons for a long time. In comparison with the SAMPEX satelli...The space particle component detector on Fengyun-1 satellite which works at the sun-synchronous orbit of about 870 km altitude has detected relativistic electrons for a long time. In comparison with the SAMPEX satellite observations during 1999–2004, the relativistic electron data from Fengyun-1 satellite from June 1999 to 2005 are used to analyze the relativistic electron enhancement (REE) events at the low earth orbit, and the possible correlation among REE events at the low earth orbit, high-speed solar wind and geomagnetic storms is discussed. The statistical result presents that 45 REE events are found in total during this time period, and the strong REE events with the maximum daily average flux > 400 cm?2·srt-1·s?1 occur mostly during the transition period from solar maximum to solar minimum. Among these 45 REE events, four strong REE events last a longer time period from 26-to 51-day and correlate closely with high speed solar wind and strong geomagnetic storms. Meanwhile, several strong geomagnetic storms occur continuously before these REE events, and these continuous geomagnetic storms would be an important factor causing these long-lasting strong REE events. The correlation analysis for overall 45 events indicates that the strength of the REE events correlates with the solar wind speed and the strength of the geomagnetic storm, and the correlation for strong REE events is much stronger than that for weak REE events.展开更多
The present paper deals with the effect of recurrent activity on the foF2 diurnal variation at Ouagadougou station for solar cycles 21 and 22. The recurrent activity produces at daytime positive storm for all solar cy...The present paper deals with the effect of recurrent activity on the foF2 diurnal variation at Ouagadougou station for solar cycles 21 and 22. The recurrent activity produces at daytime positive storm for all solar cycle phases. For all seasons, the recurrent activity causes positive storm during nighttime and has no effect during daytime. From this study, it emerges that a positive effect of the storm at this station may be explained by the thermospheric composition changes. Recurrent activity more occurs during the solar decreasing phase and during spring month. The storm strength shows solar cycle phase and seasonal dependence. The storm strength is the highest during the solar increasing phase and during summer months.展开更多
The α-particles and other heavy ions, as well as a few protons are observed to be faster than the main part of protons by about the local Alfven speed in the high-speed solar wind. It is suggested that when the veloc...The α-particles and other heavy ions, as well as a few protons are observed to be faster than the main part of protons by about the local Alfven speed in the high-speed solar wind. It is suggested that when the velocity of the solar wind is equal to the local Alfven velocity, another low-frequency kinetic Alfven wave will be excitated, and trap all the a-particles and a few protons, so these ions have a local Alfven velocity faster than the other parts of the solar wind. The undamping kinetic Alfven waves change into low-frequency Alfven solitons in the solar wind. This model can explain the observation and give the conditions of wave excitated and ions trapped.展开更多
文摘Wind and solar energy are two popular forms of renewable energy used in microgrids and facilitating the transition towards net-zero carbon emissions by 2050.However,they are exceedingly unpredictable since they rely highly on weather and atmospheric conditions.In microgrids,smart energy management systems,such as integrated demand response programs,are permanently established on a step-ahead basis,which means that accu-rate forecasting of wind speed and solar irradiance intervals is becoming increasingly crucial to the optimal operation and planning of microgrids.With this in mind,a novel“bidirectional long short-term memory network”(Bi-LSTM)-based,deep stacked,sequence-to-sequence autoencoder(S2SAE)forecasting model for predicting short-term solar irradiation and wind speed was developed and evaluated in MATLAB.To create a deep stacked S2SAE prediction model,a deep Bi-LSTM-based encoder and decoder are stacked on top of one another to reduce the dimension of the input sequence,extract its features,and then reconstruct it to produce the forecasts.Hyperparameters of the proposed deep stacked S2SAE forecasting model were optimized using the Bayesian optimization algorithm.Moreover,the forecasting performance of the proposed Bi-LSTM-based deep stacked S2SAE model was compared to three other deep,and shallow stacked S2SAEs,i.e.,the LSTM-based deep stacked S2SAE model,gated recurrent unit-based deep stacked S2SAE model,and Bi-LSTM-based shallow stacked S2SAE model.All these models were also optimized and modeled in MATLAB.The results simulated based on actual data confirmed that the proposed model outperformed the alternatives by achieving an accuracy of up to 99.7%,which evidenced the high reliability of the proposed forecasting.
文摘Wind-solar hybrid systems are employed extensively due to certain advantages. However, two problems exist in their application: the PV modules operate at high temperatures, particularly during summer, and low wind power cannot be utilized. To solve these two problems, a novel hybrid system is designed based on PV/thermal systems, in which PV modules are cooled with fans driven by a wind turbine. This paper studies the practicability of the novel hybrid system. First, the electrical performance of the wind turbine is compared using a fan and battery load,respectively. Second, different types and numbers of fans are tested to obtain the largest air volume. Third, the height of the air duct on the back of the PV module is optimized and the cooling effect is studied. Results show that a 24 V DC fan is more appropriate for the novel system than a 12 V DC fan, as it provides a greater air volume, and with a switch wind speed of 3.0 m/s the power of PV module shows a maximum increase of 8.0%.
基金National Natural Science Foundation of China(No.51478098)Innovation Foundation of Shanghai Education Commission,China(No.13ZZ054)
文摘Computational fluid dynamics( CFD) techniques are used to investigate effects of both wind direction and wind speed on net solar heat gain of south wall with internal insulation in winter.Results show that wind effect has a significant influence on the net solar heat gain,where the impact of wind direction is stronger than that of wind speed. For regions in lower reaches of the Yangtze River,difference of their average net solar heat gains( NSHGS) is about 20% due to various wind speeds and wind directions.Buildings in districts with a dominant wind direction of north achieve the highest solar energy utilization.
文摘The investment levels in electricity production capacity from variable Renewable Energy Sources have substantially grown in Brazil over the last decades,following the worldwide-seeking-goal of a carbon-neutral economy and the country’s incentives in diversifying its generation mix.From a long-term perspective,the current non-storable capability of renewable energy sources requires an adequate uncertainty characterization over the years.In this context,the main objective of this work is to provide a thorough descriptive analytics of the time-linked hourly-based daily dynamics of wind speed and solar irradiance in the main resourceful regions of Brazil.Leveraging on unsupervised Machine Learning methods,we focus on identifying similar days over the years,Representative Days,that can depict the fundamental underlying behaviour of each source.The analysis is based on a historical dataset of different sites with the highest potential and installed capacity of each source spread over the country:three in the Northeast and one in the South Regions,for wind speed;and three in the Northeast and one in the Southeast Regions,for solar irradiance.We use two Partitioning Methods(𝐾-Means and𝐾-Medoids),the Hierarchical Ward’s Method,and a Model-Based Method(Self-Organizing Maps).We identified that wind speed and solar irradiance can be effectively represented,respectively,by only two representative days,and two or three days,depending on the region and method(segments data with respect to the intensity of each source).Analysis with higher Representative Days highlighted important hidden patterns such as different wind speed modulations and solar irradiance peak-hours along the days.
文摘The space particle component detector on Fengyun-1 satellite which works at the sun-synchronous orbit of about 870 km altitude has detected relativistic electrons for a long time. In comparison with the SAMPEX satellite observations during 1999–2004, the relativistic electron data from Fengyun-1 satellite from June 1999 to 2005 are used to analyze the relativistic electron enhancement (REE) events at the low earth orbit, and the possible correlation among REE events at the low earth orbit, high-speed solar wind and geomagnetic storms is discussed. The statistical result presents that 45 REE events are found in total during this time period, and the strong REE events with the maximum daily average flux > 400 cm?2·srt-1·s?1 occur mostly during the transition period from solar maximum to solar minimum. Among these 45 REE events, four strong REE events last a longer time period from 26-to 51-day and correlate closely with high speed solar wind and strong geomagnetic storms. Meanwhile, several strong geomagnetic storms occur continuously before these REE events, and these continuous geomagnetic storms would be an important factor causing these long-lasting strong REE events. The correlation analysis for overall 45 events indicates that the strength of the REE events correlates with the solar wind speed and the strength of the geomagnetic storm, and the correlation for strong REE events is much stronger than that for weak REE events.
文摘The present paper deals with the effect of recurrent activity on the foF2 diurnal variation at Ouagadougou station for solar cycles 21 and 22. The recurrent activity produces at daytime positive storm for all solar cycle phases. For all seasons, the recurrent activity causes positive storm during nighttime and has no effect during daytime. From this study, it emerges that a positive effect of the storm at this station may be explained by the thermospheric composition changes. Recurrent activity more occurs during the solar decreasing phase and during spring month. The storm strength shows solar cycle phase and seasonal dependence. The storm strength is the highest during the solar increasing phase and during summer months.
基金This work was supported by the National Natural Science Foundation of China (Grant Nos. 49774249 & 49990452).
文摘The α-particles and other heavy ions, as well as a few protons are observed to be faster than the main part of protons by about the local Alfven speed in the high-speed solar wind. It is suggested that when the velocity of the solar wind is equal to the local Alfven velocity, another low-frequency kinetic Alfven wave will be excitated, and trap all the a-particles and a few protons, so these ions have a local Alfven velocity faster than the other parts of the solar wind. The undamping kinetic Alfven waves change into low-frequency Alfven solitons in the solar wind. This model can explain the observation and give the conditions of wave excitated and ions trapped.
