Solar power forecasting using deep learning techniques (original) (raw)
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Solar Power Prediction Using Deep Learning Technique
2017
This paper presents solar power output prediction using long shortterm memory (LSTM) network of artificial neural network. The neural network is applied to model solar power data obtained from the experimental site in the Hae-Nam, Korea. LSTM neural network with one input node, two hidden layers and one output node is used to model a day solar power data. The results obtained from the comparison of LSTM neural network and moving average indicate that the LSTM neural network approach is reasonable for short term solar power prediction.
DEEP LEARNING APPROACH BASED ON LSTM MODEL FOR SHORT-TERM SOLAR GENERATION FORECASTING
Presented at 12th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion, 2020
Due to the importance of photovoltaics (PV) generation forecasting in managing the PV output, in contemporary research community various machine learning approaches have been proposed. Deep learning is a sub-category of machine learning where a neural network is composed by more hidden layers, and thus, better training can be accomplished. The topic of deep learning based forecasting has not yet fully examined in the literature. In the present paper, the application of a Long-Term Sort Memory (LSTM) network is examined on the short-term forecasting of PV generation. A comparison with well-established machine learning algorithms is placed and conclusions are drawn.
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Neural Comput & Applic, 2017
Photovoltaic (PV) is one of the most promising renewable energy sources. To ensure secure operation and economic integration of PV in smart grids, accurate forecasting of PV power is an important issue. In this paper, we propose the use of long short-term memory recurrent neural network (LSTM-RNN) to accurately forecast the output power of PV systems. The LSTM networks can model the temporal changes in PV output power because of their recurrent architecture and memory units. The proposed method is evaluated using hourly datasets of different sites for a year. We compare the proposed method with three PV forecasting methods. The use of LSTM offers a further reduction in the forecasting error compared with the other methods. The proposed forecasting method can be a helpful tool for planning and controlling smart grids.
Solar Photovoltaic Forecasting of Power Output Using LSTM Networks
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The penetration of renewable energies has increased during the last decades since it has become an effective solution to the world’s energy challenges. Among all renewable energy sources, photovoltaic (PV) technology is the most immediate way to convert solar radiation into electricity. Nevertheless, PV power output is affected by several factors, such as location, clouds, etc. As PV plants proliferate and represent significant contributors to grid electricity production, it becomes increasingly important to manage their inherent alterability. Therefore, solar PV forecasting is a pivotal factor to support reliable and cost-effective grid operation and control. In this paper, a stacked long short-term memory network, which is a significant component of the deep recurrent neural network, is considered for the prediction of PV power output for 1.5 h ahead. Historical data of PV power output from a PV plant in Nicosia, Cyprus, were used as input to the forecasting model. Once the model ...
Hybrid deep learning models for time series forecasting of solar power
Forecasting solar power production accurately is critical for effectively planning and managing renewable energy systems. This paper introduces and investigates novel hybrid deep learning models for solar power forecasting using time series data. The research analyzes the efficacy of various models for capturing the complex patterns present in solar power data. In this study, all of the possible combinations of convolutional neural network (CNN), long short-term memory (LSTM), and transformer (TF) models are experimented. These hybrid models also compared with the single CNN, LSTM and TF models with respect to different kinds of optimizers. Three different evaluation metrics are also employed for performance analysis. Results show that the CNN-LSTM-TF hybrid model outperforms the other models, with a mean absolute error (MAE) of 0.551% when using the Nadam optimizer. However, the TF-LSTM model has relatively low performance, with an MAE of 16.17%, highlighting the difficulties in making reliable predictions of solar power. This result provides valuable insights for optimizing and planning renewable energy systems, highlighting the significance of selecting appropriate models and optimizers for accurate solar power forecasting. This is the first time such a comprehensive work presented that also involves transformer networks in hybrid models for solar power forecasting.
Deep and Machine Learning Models to Forecast Photovoltaic Power Generation
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The integration and management of distributed energy resources (DERs), including residential photovoltaic (PV) production, coupled with the widespread use of enabling technologies such as artificial intelligence, have led to the emergence of new tools, market models, and business opportunities. The accurate forecasting of these resources has become crucial to decision making, despite data availability and reliability issues in some parts of the world. To address these challenges, this paper proposes a deep and machine learning-based methodology for PV power forecasting, which includes XGBoost, random forest, support vector regressor, multi-layer perceptron, and LSTM-based tuned models, and introduces the ConvLSTM1D approach for this task. These models were evaluated on the univariate time-series prediction of low-volume residential PV production data across various forecast horizons. The proposed benchmarking and analysis approach considers technical and economic impacts, which can ...
Solar Irradiance Forecasting using Deep Learning Approaches
National Undergraduate Research Symposium of National Science and Technology Commission (NASTEC), 2019
The purpose of this study is to come up with a most accurate model for predicting the Solar photovoltaic (PV) power generation and Solar irradiance. For this study, the data is collected from Faculty of Engineering, University of Jaffa solar measuring station. In this paper, deep learning based univariate long short-term memory (LSTM) approach is introduced to predict the Solar irradiance. A univariate LSTM and auto-regressive integrated moving average (ARIMA) based time series approaches are compared. Both models are evaluated using root mean-square error (RMSE). This study suggests that univariate LSTM approach performs well over ARIMA approach.
ASEC 2023
Photovoltaic (PV)-system-generated solar energy has inconsistent and variable properties, which makes controlling electric power distribution and preserving grid stability extremely difficult. A photovoltaic (PV) system's performance is profoundly affected by the amount of sunlight that reaches the solar cell, the season of the year, the ambient temperature, and the humidity of the air. Every renewable energy technology, sadly, has its problems. As a result, the system is unable to function at its highest or best level. To combat the unstable and intermittent performance of solar power output, it is essential to achieve a precise PV system output power. This work introduces a new approach to enhancing accuracy and extending the time range of very short-term solar energy forecasting (15 min step ahead) by using multivariate time series inputs in different seasons. First, Linear Discriminant Analysis (LDA) is used to select the relevant factors from the mixed meteorological input data. Secondly, two very short-term deep learning prediction models, CNN and LSTM, are used to predict PV power for a shuffled and reduced database of weather inputs. Finally, the predicted outputs from the two models are combined using a classification strategy. The proposed method is applied to one year of real data collected from a solar power plant located in southern Algeria to demonstrate that this technique can improve the forecasting accuracy compared to other techniques, as determined through the use of statistical analysis involving normalized root mean square error (NRMSE), mean absolute error (MAE), mean bias error (MBE), and determination coefficient. (R 2 ).
Predicting the Performance of Solar Power Generation Using Deep Learning Methods
2021
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, ...