Hourly Solar Irradiance Forecasting Based on Machine Learning Models

Melzi, Fateh Nassim; Touati, Taieb; Samé, Allou; Oukhellou, Latifa

doi:10.1109/icmla.2016.0078

Cited by 31 publications

(10 citation statements)

References 17 publications

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“…Physics-based models, including Numerical Weather Prediction (NWP) models, use satellite and ground-based sky image data to evaluate cloud formations and predict solar irradiance [11,12].While physics-based models explain causality through closed-form equations or numerical simulations, the solutions are in some cases limited with respect to their computational tractability [1]. On the other hand, statistics-based models, including Regression [13], Exponential Smoothing Models [14], and variants of the Autoregressive Integrated Moving Average (ARIMA) model [4,13,15], have been implemented for solar irradiance forecasting over shorter horizons. These models use statistical analysis in order to map inputs to the forecast variable; however, considering the non-stationary nature of solar irradiance data, statistical models fail to accurately predict sudden changes in solar irradiance [13].…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning models including Artificial Neural Networks have the ability to train the model using historical solar irradiance data, exogenous meteorological data, or both to capture the stochasticity of solar irradiance data. Melzi et al [15] compared the performance of several machine learning techniques to statistical models using previous hours of solar irradiance and previous days containing the same number of daylight hours in order to forecast hourly solar irradiance. They found that combining exogenous variables to solar irradiance data improved model performance.…”

Section: Introductionmentioning

confidence: 99%

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Hour-Ahead Solar Irradiance Forecasting Using Multivariate Gated Recurrent Units

et al. 2019

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hour-Ahead Solar Irradiance Forecasting Using Multivariate Gated Recurrent Units

et al. 2019

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“…Солнце является неисчерпаемым источником энергии, но приходящая на подстилающую поверхность энергия солнечного излучения (СИ) обладает большой временной изменчивостью (суточной и сезонной) и зависит от места локализации станций и метеосостояния атмосферы [5,6]. В свете выше изложенного для оптимизации конструкции солнечных тепловых станций (СТС) и солнечных электрических станций (СЭС) требуется знание зависимостей световой энергии солнечного излучения [7,8], поступающего на световоспринимающую поверхность котельной установки, в зависимости от метеосостояния атмосферы по месту дислокации станции и времени работы станции [9][10][11]. В настоящей работе рассмотрено получение необходимых для разработки конструкций СТС и СЭС исходных данных методом численного моделирования приходящих потоков СИ в спектральной области их функционирования.…”

Section: Introductionunclassified

Simulation of the impact of the atmospheric weather state on the efficiency of functioning of solar thermal and power plants

Москаленко

Akhmetshin

Safiullina

et al. 2022

«Izvestiya vysshikh uchebnykh zavedenii. PROBLEMY ENERGETIKI»

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THE PURPOSE. Determine the impact of the meteorological state of the atmosphere on the efficiency of the functioning of solar thermal and power plants. Modeling the molecular absorption of solar radiation by the atmosphere. Modeling the optical characteristics of the gaseous components of the atmosphere, atmospheric aerosol and clouds.METHODS. A method for numerical modeling of incoming solar radiation fluxes their functioning to determine the efficiency of solar thermal and power plants. The solar fluxes are calculated by stacking layers in a multi-stream approximation, taking into account the multi-tiered cloud cover and the probability of overlapping the sky with clouds. The absorption of radiation by the gaseous phase of the atmosphere is taken into account by the method of equivalent mass in an inhomogeneous atmosphere. The optical characteristics of the dispersed phase of the atmosphere are calculated using the Mie theory.RESULTS. An electronic database has been created on the optical characteristics of the gaseous components of the atmosphere, the optical characteristics of atmospheric aerosol and clouds. The effect of anthropogenic impact on the flux of solar radiation falling on the underlying surface is taken into account. The developed modeling takes into account the effect of humidity on the optical characteristics of atmospheric aerosol and its multicomponent composition, depending on the location of the power plant.CONCLUSION. The information necessary for numerical modeling of meteorological effects on the functioning of solar thermal and power plants is generalized. When calculating solar radiation fluxes, direct illumination of the light-receiving surface by solar radiation, scattered radiation by atmospheric aerosol and clouds are taken into account.

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“…A detailed review study on wind speed and solar irradiance forecasting based on ensemble techniques was presented in [52]. The seasonal strategy was reported based on the auto regressive integrated moving average (ARIMA) method for irradiance forecasting [53]. The applicability and limitations of machine learning models for solar irradiance forecasting for the day ahead and a few hours ahead prediction scales were reported [54].…”

Section: Introductionmentioning

confidence: 99%

Novel Multi-Time Scale Deep Learning Algorithm for Solar Irradiance Forecasting

Jayalakshmi

Shankar²,

Subramaniam

et al. 2021

Energies

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Solar irradiance forecasting is an inevitable and most significant process in grid-connected photovoltaic systems. Solar power is highly non-linear, and thus to manage the grid operation efficiently, with irradiance forecasting for various timescales, such as an hour ahead, a day ahead, and a week ahead, strategies are developed and analysed in this article. However, the single time scale model can perform better for that specific time scale but cannot be employed for other time scale forecasting. Moreover, the data consideration for single time scale forecasting is limited. In this work, a multi-time scale model for solar irradiance forecasting is proposed based on the multi-task learning algorithm. An effective resource sharing scheme between each task is presented. The proposed multi-task learning algorithm is implemented with a long short-term memory (LSTM) neural network model and the performance is investigated for various time scale forecasting. The hyperparameter estimation of the proposed LSTM model is made by a hybrid chicken swarm optimizer based on combining the best features of both the chicken swarm optimization algorithm (CSO) and grey wolf optimization (GWO) algorithm. The proposed model is validated, comparing existing methodologies for single timescale forecasting, and the proposed strategy demonstrated highly consistent performance for all time scale forecasting with improved metric results.

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Hourly Solar Irradiance Forecasting Based on Machine Learning Models

Cited by 31 publications

References 17 publications

Hour-Ahead Solar Irradiance Forecasting Using Multivariate Gated Recurrent Units

Hour-Ahead Solar Irradiance Forecasting Using Multivariate Gated Recurrent Units

Simulation of the impact of the atmospheric weather state on the efficiency of functioning of solar thermal and power plants

Novel Multi-Time Scale Deep Learning Algorithm for Solar Irradiance Forecasting

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