Prediction of Output Solar Power Generation using Neural Network Time Series Method

Natural rubber is one of the most important crops in Malaysia alongside palm oil, cocoa, paddy, and pineapple. Being a tropical country, Malaysia is one of the top five exporters and producers of rubber in the world. The purpose of this study is to find the forecasted value of the actual data of the number of exportations of natural rubber by using Fuzzy Time Series and Artificial Neural Network. This study is also conducted to determine the best model by making comparison between Fuzzy Time Series and Artificial Neural Network. Fuzzy Time Series has allowed to overcome a downside where the classical time series method cannot deal with forecasting problem in which values of time series are linguistic terms represented by fuzzy sets. Artificial Neural Network was introduced as one of the systematic tools of modelling which has been forecasting for about 20 years ago. The error measure that was used in this study to make comparisons were Mean Square Error, Root Mean Square Error and Mean Absolute Percentage Error. The results of this study showed that the fuzzy time series method has the smallest error value compared to artificial neural network which means it was more accurate compared to artificial neural network in forecasting exportation of natural rubber in Malaysia.

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Neural Network Ensemble-Based Solar Power Generation Short-Term Forecasting

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2010.p0069 ◽

2010 ◽

Vol 14 (1) ◽

pp. 69-75 ◽

Cited By ~ 27

Author(s):

Aymen Chaouachi ◽

◽

Rashad M. Kamel ◽

Ken Nagasaka

Keyword(s):

Neural Network ◽

Neural Networks ◽

Power Generation ◽

Solar Power ◽

Photovoltaic System ◽

Neural Network Ensemble ◽

Error Performance ◽

Solar Power Generation ◽

The Neural Network ◽

Short Term Forecasting

This paper presents the applicability of artificial neural networks for 24 hour ahead solar power generation forecasting of a 20 kW photovoltaic system, the developed forecasting is suitable for a reliable Microgrid energy management. In total four neural networks were proposed, namely: multi-layred perceptron, radial basis function, recurrent and a neural network ensemble consisting in ensemble of bagged networks. Forecasting reliability of the proposed neural networks was carried out in terms forecasting error performance basing on statistical and graphical methods. The experimental results showed that all the proposed networks achieved an acceptable forecasting accuracy. In term of comparison the neural network ensemble gives the highest precision forecasting comparing to the conventional networks. In fact, each network of the ensemble over-fits to some extent and leads to a diversity which enhances the noise tolerance and the forecasting generalization performance comparing to the conventional networks.

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Forecasting Solar Power Generation Using Fuzzy Logic and Artificial Neural Network

2019 Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South Africa (SAUPEC/RobMech/PRASA) ◽

10.1109/robomech.2019.8704737 ◽

2019 ◽

Author(s):

Z.P. Ncane ◽

A.K. Saha

Keyword(s):

Neural Network ◽

Power Generation ◽

Artificial Neural Network ◽

Fuzzy Logic ◽

Solar Power ◽

Solar Power Generation ◽

Artificial Neural

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Quantifying Uncertainty for Predicting Renewable Energy Time Series Data Using Machine Learning

Engineering Proceedings ◽

10.3390/engproc2021005050 ◽

2021 ◽

Vol 5 (1) ◽

pp. 50

Author(s):

Phil Aupke ◽

Andreas Kassler ◽

Andreas Theocharis ◽

Magnus Nilsson ◽

Michael Uelschen

Keyword(s):

Machine Learning ◽

Power Generation ◽

Time Series ◽

Renewable Energy ◽

Solar Power ◽

Time Series Data ◽

Series Data ◽

Solar Power Generation ◽

Renewable Energy Generation ◽

Micro Grids

Recently, there has been growing interest in using machine learning based methods for forecasting renewable energy generation using time-series prediction. Such forecasting is important in order to optimize energy management systems in future micro-grids that will integrate a large amount of solar power generation. However, predicting solar power generation is difficult due to the uncertainty of the solar irradiance and weather phenomena. In this paper, we quantify the impact of uncertainty of machine learning based time-series predictors on the forecast accuracy of renewable energy generation using long-term time series data available from a real micro-grid in Sweden. We use clustering to build different ML forecasting models using LSTM and Facebook Prophet. We evaluate the accuracy impact of using interpolated weather and radiance information on both clustered and non-clustered models. Our evaluations show that clustering decreases the uncertainty by more than 50%. When using actual on-side weather information for the model training and interpolated data for the inference, the improvements in accuracy due to clustering are the highest, which makes our approach an interesting candidate for usage in real micro-grids.

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