scholarly journals Hour-Ahead Solar Irradiance Forecasting Using Multivariate Gated Recurrent Units

Energies ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 4055 ◽  
Author(s):  
Jessica Wojtkiewicz ◽  
Matin Hosseini ◽  
Raju Gottumukkala ◽  
Terrence Lynn Chambers
Keyword(s):  
Power Generation ◽  
Solar Irradiance ◽  
Cloud Cover ◽  
Power Plants ◽  
Solar Power ◽  
Short Term Memory ◽  
Time Of Day ◽  
Sequential Data ◽  
Weather Variables ◽  
Gated Recurrent Units

Variation in solar irradiance causes power generation fluctuations in solar power plants. Power grid operators need accurate irradiance forecasts to manage this variability. Many factors affect irradiance, including the time of year, weather and time of day. Cloud cover is one of the most important variables that affects solar power generation, but is also characterized by a high degree of variability and uncertainty. Deep learning methods have the ability to learn long-term dependencies within sequential data. We investigate the application of Gated Recurrent Units (GRU) to forecast solar irradiance and present the results of applying multivariate GRU to forecast hourly solar irradiance in Phoenix, Arizona. We compare and evaluate the performance of GRU against Long Short-Term Memory (LSTM) using strictly historical solar irradiance data as well as the addition of exogenous weather variables and cloud cover data. Based on our results, we found that the addition of exogenous weather variables and cloud cover data in both GRU and LSTM significantly improved forecasting accuracy, performing better than univariate and statistical models.

scholarly journals Solar Irradiance Nowcasting for Virtual Power Plants Using Multimodal Long Short-Term Memory Networks

2021 ◽  
Vol 9 ◽  
Author(s):  
Dilantha Haputhanthri ◽  
Daswin De Silva ◽  
Seppo Sierla ◽  
Damminda Alahakoon ◽  
Rashmika Nawaratne ◽  
...  
Keyword(s):  
Solar Irradiance ◽  
Cloud Cover ◽  
Power Plants ◽  
Short Term Memory ◽  
Virtual Power ◽  
Short Term ◽  
Photovoltaic Generation ◽  
Term Memory ◽  
Virtual Power Plants ◽  
Long Short Term Memory

The rapid penetration of photovoltaic generation reduces power grid inertia and increases the need for intelligent energy resources that can cope in real time with the imbalance between power generation and consumption. Virtual power plants are a technology for coordinating such resources and monetizing them, for example on electricity markets with real-time pricing or on frequency reserves markets. Accurate short-term photovoltaic generation forecasts are essential for such virtual power plants. Although significant research has been done on medium- and long-term photovoltaic generation forecasting, the short-term forecasting problem requires special attention to sudden fluctuations due to the high variability of cloud cover and related weather events. Solar irradiance nowcasting aims to resolve this variability by providing reliable short-term forecasts of the expected power generation capacity. Sky images captured in proximity to the photovoltaic panels are used to determine cloud behavior and solar intensity. This is a computationally challenging task for conventional computer vision techniques and only a handful of Artificial Intelligence (AI) methods have been proposed. In this paper, a novel multimodal approach is proposed based on two Long Short-Term Memory Networks (LSTM) that receives a temporal image modality of a stream of sky images, a temporal numerical modality of a time-series of past solar irradiance readings and cloud cover readings as inputs for irradiance nowcasting. The proposed nowcasting pipeline consists of a preprocessing module and an irradiance augmentation module that implements methods for cloud detection, Sun localization and mask generation. The complete approach was empirically evaluated on a real-world solar irradiance case study across the four seasons of the northern hemisphere, resulting in a mean improvement of 39% for multimodality.

scholarly journals Predicting the Performance of Solar Power Generation Using Deep Learning Methods

2021 ◽  
Vol 11 (15) ◽  
pp. 6887
Author(s):  
Chung-Hong Lee ◽  
Hsin-Chang Yang ◽  
Guan-Bo Ye
Keyword(s):  
Power Generation ◽  
Solar Energy ◽  
Power Output ◽  
Power Plants ◽  
Solar Power ◽  
Photovoltaic Cell ◽  
Power Prediction ◽  
Seasonal Characteristics ◽  
Variable Nature ◽  
Solar Power Plants

In recent years, many countries have provided promotion policies related to renewable energy in order to take advantage of the environmental factors of sufficient sunlight. However, the application of solar energy in the power grid also has disadvantages. The most obvious is the variability of power output, which will put pressure on the system. As more grid reserves are needed to compensate for fluctuations in power output, the variable nature of solar power may hinder further deployment. Besides, one of the main issues surrounding solar energy is the variability and unpredictability of sunlight. If it is cloudy or covered by clouds during the day, the photovoltaic cell cannot produce satisfactory electricity. How to collect relevant factors (variables) and data to make predictions so that the solar system can increase the power generation of solar power plants is an important topic that every solar supplier is constantly thinking about. The view is taken, therefore, in this work, we utilized the historical monitoring data collected by the ground-connected solar power plants to predict the power generation, using daily characteristics (24 h) to replace the usual seasonal characteristics (365 days) as the experimental basis. Further, we implemented daily numerical prediction of the whole-point power generation. The preliminary experimental evaluations demonstrate that our developed method is sensible, allowing for exploring the performance of solar power prediction.

scholarly journals ASSESSMENT OF SURFACE DOWNWELLING SHORTWAVE RADIATION IN 2021-2050 IN LAAYOUNE − SAKIA EL HAMRA REGION, MOROCCO

2019 ◽  
Vol 2 ◽  
pp. 23-29
Author(s):  
Youssef El Hadri ◽  
Valeriy Khokhlov ◽  
Mariia Slizhe ◽  
Kateryna Sernytska ◽  
Kateryna Stepanova
Keyword(s):  
Solar Energy ◽  
Spatial Resolution ◽  
Cloud Cover ◽  
Power Plants ◽  
Solar Power ◽  
Regional Climate ◽  
Energy Resources ◽  
Shortwave Radiation ◽  
Total Cloud Cover ◽  
Solar Power Plants

Morocco's energy system is highly dependent on external energy markets. According to the Ministry Energy, Mines and Sustainable Development today more than 93 % of energy resources are imported to Morocco. In 2008 the Moroccan Government has developed a National Energy Strategy, and one of its priority areas is to increase the share of renewable technologies in the country's energy sector. Morocco is rich in solar energy resources. Studies on the assessment of the Morocco’s solar energy potential indicate, among other benefits, low additional costs when using solar installations compared to losses associated with the solution of future climate problems and lack of resources. The plan envisages the commissioning of solar power plants in Ouarzazate, Ain Ben Mathar, Boujdour, Tarfaya and Laayoune by 2020. The aim of this research is determination of the characteristics of the distribution of Surface Downwelling Shortwave Radiation in the area of the solar power Boujdour, Tarfaya and Laayoune, located in the Laayoune − Sakia El Hamra region in 2021−2050. The data from regional climate modeling with high spatial resolution of the CORDEX-Africa project are used in this research. The RCM modeling is carried out for the region of Africa, in a rectangular coordinate system with a spatial resolution of ~ 44 km. Then, from the modeling data, values are highlighted for the territory of Laayoune − Sakia El Hamra region. Model calculation is performed taking into account the greenhouse gas concentration trajectory of RCP 4.5 calculated using 11 regional climate models. As a result of the simulation for the period 2021−2050, average monthly values of the Surface Downwelling Shortwave Radiation "RSDS" (W/m2) are derived, on the basis of which the mean values for the period of time are calculated. For more detailed information, average monthly total cloud cover values "TC" (%) for the period under study are calculated. Analysis of the change in RSDS in 2021–2050 relative to the recent climatic period is shown that in the Laayoune − Sakia El Hamra region we can expect an increase or retention of its values. The annual run of the RSDS has one maximum in June and one minimum in December. In the future, the distribution of RSDS in the Laayoune − Sakia El Hamra region will have a significant impact on proximity to the Atlantic Ocean, where an increased amount of total cloud cover significantly reduces the amount of incoming radiation. In the location of solar power plants in the near future, the current RSDS values are expected to be maintained, which creates favorable conditions for the further development of the renewable energy industry in this area and increasing its productivity.

scholarly journals EFFECT OF TEMPERATURE, HUMIDITY AND IRRADIANCE ON SOLAR POWER GENERATION

2021 ◽  
Vol 26 (4) ◽  
pp. 113-119
Author(s):  
FRANK ONAIFO ◽  
AKPOFURE ALEXANDER OKANDEJI ◽  
OLAMIDE AJETUNMOBI ◽  
DAVID BALOGUN
Keyword(s):  
Power Generation ◽  
Solar Cells ◽  
Solar Cell ◽  
Solar Radiation ◽  
Solar Irradiance ◽  
Solar Power ◽  
Corrosion Process ◽  
Effect Of Temperature ◽  
Photovoltaic Solar Cell ◽  
Solar Power Generation

This paper studies the effect of temperature, humidity and irradiance on the power generated by a photovoltaic solar cell. This was achieved using pyranometer for determining the solar radiation, wet and dry thermometer for measuring humidity, and digital multimeter for voltage and current measurement. The result of the study show that power generation increases with increase of solar irradiance. Additionally, changes of humidity level and temperature do not significantly affect solar power generation. Furthermore, it was also observed that high temperatures and higher humidity levels accelerate the corrosion process on the solar cells which reduces the efficiency of the cells.

scholarly journals Towards the Development of a Low-Cost Irradiance Nowcasting Sky Imager

2019 ◽  
Vol 9 (6) ◽  
pp. 1131 ◽  
Author(s):  
Luis Valentín ◽  
Manuel Peña-Cruz ◽  
Daniela Moctezuma ◽  
Cesar Peña-Martínez ◽  
Carlos Pineda-Arellano ◽  
...  
Keyword(s):  
Solar Irradiance ◽  
Power Plants ◽  
Solar Power ◽  
Detection System ◽  
Low Cost ◽  
Resource Assessment ◽  
The Sun ◽  
Robust Detection ◽  
Solar Power Plants ◽  
Solar Resource

Solar resource assessment is fundamental to reduce the risk in selecting the solar power-plants’ location; also for designing the appropriate solar-energy conversion technology and operating new sources of solar-power generation. Having a reliable methodology for solar irradiance forecasting allows accurately identifying variations in the plant energy production and, as a consequence, determining improvements in energy supply strategies. A new trend for solar resource assessment is based on the analysis of the sky dynamics by processing a set of images of the sky dome. In this paper, a methodology for processing the sky dome images to obtain the position of the Sun is presented; this parameter is relevant to compute the solar irradiance implemented in solar resource assessment. This methodology is based on the implementation of several techniques in order to achieve a combined, fast, and robust detection system for the Sun position regardless of the conditions of the sky, which is a complex task due to the variability of the sky dynamics. Identifying the correct position of the Sun is a critical parameter to project whether, in the presence of clouds, the occlusion of the Sun is occurring, which is essential in short-term solar resource assessment, the so-called irradiance nowcasting. The experimental results confirm that the proposed methodology performs well in the detection of the position of the Sun not only in a clear-sky day, but also in a cloudy one. The proposed methodology is also a reliable tool to cover the dynamics of the sky.

scholarly journals Survey Analysis of Solar Power Generation Forecasting

2021 ◽  
Vol 309 ◽  
pp. 01039
Author(s):  
Deekshitha Erlapally ◽  
K. Anuradha ◽  
G. Karuna ◽  
V. Srilakshmi ◽  
K. Adilakshmi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Power Generation ◽  
Deep Learning ◽  
Power Plants ◽  
Solar Power ◽  
Weather Condition ◽  
Series Data ◽  
Mean Bias Error ◽  
Percentage Error

Solar power is the conversion of sunlight into electricity using solar photovoltaic cells as a source of energy. There are various applications for solar power; here is information on PV cell generation. We seek to understand the behavior of solar power plants through the data generated by the photovoltaic modules and the power generation in different weather conditions in India. The goal of this survey is to give a thorough assessment and study of machine learning, deep learning and artificial intelligence. Artificial intelligence (AI) models as well as information preprocessing techniques, parameter selection algorithms and predictive performance evaluations are used in machine learning and deep learning models for predicting renewable energies. But in case of time series data we can predict only the errors using a linear regression model, we can also calculate things like root mean square error (RMSE), mean absolute error (MSE), mean bias error (MBE) and mean absolute percentage error (MAPE). By the analysis of weather condition also we can predict the consumption of current by solar for every 15 minutes, 1day, and 1week or even for 1 month and find the accuracy.

Satellite-Based Operational Solar Irradiance Forecast for Uruguay's Solar Power Plants

2021 ◽  
Author(s):  
Rodrigo Alonso-Suarez ◽  
Franco Marchesoni ◽  
Liber Dovat ◽  
Agustin Laguarda
Keyword(s):  
Solar Irradiance ◽  
Power Plants ◽  
Solar Power ◽  
Solar Power Plants ◽  
Irradiance Forecast

Efficiency Improvement of Ground-Mounted Solar Power Generation in Agrivoltaic System by Cultivation of Bok Choy (Brassica rapa subsp. chinensis L.) Under the Panels

2021 ◽  
Vol 11 (1) ◽  
pp. 103-110
Author(s):  
Manoch Kumpanalaisatit ◽  
Worajit Setthapun ◽  
Hathaitip Sintuya ◽  
Surachai Narrat Jansri
Keyword(s):  
Power Generation ◽  
Brassica Rapa ◽  
Crop Production ◽  
Power Plants ◽  
Solar Power ◽  
Control Plot ◽  
Growth And Yield ◽  
Average Intensity ◽  
Solar Panels ◽  
Solar Power Generation

An agrivoltaic system is a combination of solar power generation and crop production that has the potential to increase the value of land. The system was carried out at a 25-kW photovoltaic (PV) power plant located at the Asian Development College for Community Economy and Technology (adiCET), Chiang Mai Rajabhat University, Thailand. The growth and yield of bok choy (Brassica rapa subsp. chinensis L.) and the solar power output were investigated and compared with the control. Moreover, the efficiency of the agrivoltaic system was evaluated. The results indicated that the average intensity of solar radiation of 569 W/m2 was obtained. The highest power generation was recorded in the PV with crop production of 2.28 kW. Furthermore, the control plot of crop production at 35 days provided higher growth than bok choy plots under solar panels of 2.1 cm in plant height, 6 in leaf number, 2.2 cm in leaf length and 0.2 cm in leaf wide. High-yield of bok choy was also obtained in the control plot of 17.31 kg. Although the yield of bok choy is extremely low, possibly because of light intensity, crop cultivation under solar panels could reduce the module temperature to less than the PV control of 0.18 °C, resulting in increased voltage and power generation by around 0.09 %. Therefore, an agrivoltaic system is another option for increasing revenue and land equivalent ratio in solar power plants focusing only on electricity generation. However, suitable crops for the space under PV panels should be investigated further.

scholarly journals From Firm Solar Power Forecasts to Firm Solar Power Generation: An Effective Path to Ultra-High Renewable Penetration - A New York Case Study

2020 ◽  
Author(s):  
Richard Perez ◽  
Marc Perez ◽  
Sergey Kivalov ◽  
James Schlemmer ◽  
John Dise ◽  
...  
Keyword(s):  
Power Generation ◽  
New York ◽  
Power Plants ◽  
Solar Power ◽  
Production Costs ◽  
Electricity Production ◽  
Prediction Errors ◽  
Firm Power ◽  
Solar Penetration

We introduce firm solar forecasts as a strategy to operate optimally overbuilt solar power plants in conjunction with optimally sized storage systems so as to make up for any power prediction errors, hence entirely remove load balancing uncertainty emanating from grid-connected solar fleets. A central part of this strategy is plant overbuilding that we term implicit storage. We show that strategy, while economically justifiable on its own account, is an effective entry step to least-cost ultra-high solar penetration where firm power generation will be a prerequisite. We demonstrate that in absence of an implicit storage strategy, ultra-high solar penetration would be vastly more expensive. Using the New York Independent System Operator (NYISO) as a case study, we determine current and future cost of firm forecasts for a comprehensive set of scenarios in each ISO electrical region, comparing centralized vs. decentralized production and assessing load flexibility’s impact. We simulate the growth of the strategy from firm forecast to firm power generation. We conclude that ultra-high solar penetration enabled by the present strategy, whereby solar would firmly supply the entire NYISO load, could be achieved locally at electricity production costs comparable to current NYISO wholesale market prices.

Sign in / Sign up

Export Citation Format

Share Document