Proceedings ArticleDOI
Solar PV Generation Forecast Model Based on the Most Effective Weather Parameters
TLDR
In this article, the authors proposed a forecasting model based on ANN with forecasted and historical weather parameters (i.e., temperature, dew point, relative humidity and wind speed) as inputs.Abstract:
Solar energy is one of the major renewable energy sources with the potential to cope with the future energy challenges. But the penetration of solar PV generation in the electrical grid is a serious concern because of variable availability. Therefore, solar PV generation forecasting is essential for planning and efficient operation. The forecasting model is based on Artificial Neural Network (ANN) with forecasted and historical weather parameters i.e., temperature, dew point, relative humidity and wind speed as inputs. The aim of this study is to determine the most effective combination of weather variables to be used as input to the model. For this, all the possible combinations of the inputs are applied to ANN and the best one is obtained by analysis of the results. Mean Absolute Percentage Error (MAPE) is used as a measure to compare the results. To train the ANN model, one year's weather and generation data of 20.8 kW PV system with an hourly resolution is used. 24 hours ahead forecasting of the generation is done using forecasted weather data of 14 days selected from the dataset of 130 days. Combination of three parameters (temperature, relative humidity and dew point) results in an average MAPE of 14.86% while the use of all four parameters as inputs gives 14.33% of MAPE.read more
Citations
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Journal ArticleDOI
Solar Radiation Forecasting by Pearson Correlation Using LSTM Neural Network and ANFIS Method: Application in the West-Central Jordan
Hossam Fraihat,Amneh A. Almbaideen,Abdullah Al-Odienat,Bassam Al-Naami,Roberto de Fazio,Paolo Visconti +5 more
TL;DR: In this article , the influence of these parameters on predicting solar radiation and electric energy produced in the Salt-Jordan region (Middle East) using long short-term memory (LSTM) and adaptive network-based fuzzy inference system (ANFIS) models.
Journal ArticleDOI
A Comparison Between Deep Learning and Support Vector Regression Techniques Applied to Solar Forecast in Spain
Marcello Anderson F. B. Lima,Luis M. Fernández Ramírez,Paulo Cesar Marques de Carvalho,Josias G. Batista,Deivid Matias de Freitas +4 more
TL;DR: Two machine learning techniques are presented and compared, deep learning (DL) and support vector regression (SVR), to verify their behavior for solar forecasting, and the DL achieved the best results for solar energy forecast.
Journal ArticleDOI
Techno-economic analysis and energy forecasting study of domestic and commercial photovoltaic system installations in Estonia
TL;DR: In this article , a case study of solar irradiance and energy generation potential in different regions of Estonia is presented, where an efficient deep learning-based forecasting algorithm is used for short-term energy management.
Journal ArticleDOI
MPF-Net: A computational multi-regional solar power forecasting framework
Faiza Mehmood,Muhammad Usman Ghani,Muhammad Nabeel Asim,Muhammad Nabeel Asim,Rehab Shahzadi,Aamir Mehmood,Waqar Mahmood +6 more
TL;DR: Among machine and deep learning based regressors, proposed meta-regressor along with optimal subset of feature/s achieves the best R 2 score of 98% for 6 regions and 97% for other 3 regions of Pakistan.
Proceedings ArticleDOI
The Solar Energy Forecasting Using LSTM Deep Learning Technique
TL;DR: In this article , a wide range of features are considered in the forecasting process, including root mean squared error (RMSE) and mean square error (MSE) are used for evaluation.
References
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Book
Artificial Neural Networks
TL;DR: artificial neural networks, artificial neural networks , مرکز فناوری اطلاعات و اصاع رسانی, کδاوρزی
Journal ArticleDOI
Ensemble methods for wind and solar power forecasting—A state-of-the-art review
TL;DR: In this paper, state-of-the-art on wind speed/power forecasting and solar irradiance forecasting with ensemble methods are reviewed and compared based on reported results and comparisons based on simulations conducted by us.
Journal ArticleDOI
Intermittent and stochastic character of renewable energy sources: Consequences, cost of intermittence and benefit of forecasting
Gilles Notton,Marie Laure Nivet,Cyril Voyant,Christophe Paoli,Christophe Darras,Fabrice Motte,Alexis Fouilloy +6 more
TL;DR: In this paper, the authors synthesize the reasons to predict solar or wind fluctuations, it shows that variability and stochastic variation of renewable sources have a cost, sometimes high, and provides useful information on the intermittence cost and on the decreasing of this cost due to an efficient forecasting of the source fluctuation.
Journal ArticleDOI
Analysis and validation of 24 hours ahead neural network forecasting of photovoltaic output power
TL;DR: The hourly energy prediction covers all the daylight hours of the following day, based on 48źhours ahead weather forecast, very important due to the predictive features requested by smart grid application: renewable energy sources planning, in particular storage system sizing, and market of energy.
Journal ArticleDOI
A review on the selected applications of forecasting models in renewable power systems
Adil Ahmed,Muhammad Khalid +1 more
TL;DR: A literature review on the selected applications of renewable resource and power forecasting models to facilitate the optimal integration of renewable energy in power systems and the impact of forecasting improvement on optimal power system design and operation is presented.