7,059 research outputs found
Innovative Second-Generation Wavelets Construction With Recurrent Neural Networks for Solar Radiation Forecasting
Solar radiation prediction is an important challenge for the electrical
engineer because it is used to estimate the power developed by commercial
photovoltaic modules. This paper deals with the problem of solar radiation
prediction based on observed meteorological data. A 2-day forecast is obtained
by using novel wavelet recurrent neural networks (WRNNs). In fact, these WRNNS
are used to exploit the correlation between solar radiation and
timescale-related variations of wind speed, humidity, and temperature. The
input to the selected WRNN is provided by timescale-related bands of wavelet
coefficients obtained from meteorological time series. The experimental setup
available at the University of Catania, Italy, provided this information. The
novelty of this approach is that the proposed WRNN performs the prediction in
the wavelet domain and, in addition, also performs the inverse wavelet
transform, giving the predicted signal as output. The obtained simulation
results show a very low root-mean-square error compared to the results of the
solar radiation prediction approaches obtained by hybrid neural networks
reported in the recent literature
An innovative metaheuristic strategy for solar energy management through a neural networks framework
Proper management of solar energy as an effective renewable source is of high importance toward sustainable energy harvesting. This paper offers a novel sophisticated method for predicting solar irradiance (SIr) from environmental conditions. To this end, an efficient metaheuristic technique, namely electromagnetic field optimization (EFO), is employed for optimizing a neural network. This algorithm quickly mines a publicly available dataset for nonlinearly tuning the network parameters. To suggest an optimal configuration, five influential parameters of the EFO are optimized by an extensive trial and error practice. Analyzing the results showed that the proposed model can learn the SIr pattern and predict it for unseen conditions with high accuracy. Furthermore, it provided about 10% and 16% higher accuracy compared to two benchmark optimizers, namely shuffled complex evolution and shuffled frog leaping algorithm. Hence, the EFO-supervised neural network can be a promising tool for the early prediction of SIr in practice. The findings of this research may shed light on the use of advanced intelligent models for efficient energy development
Wind Power Forecasting Methods Based on Deep Learning: A Survey
Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid. Aiming to provide reference strategies for relevant researchers as well as practical applications, this paper attempts to provide the literature investigation and methods analysis of deep learning, enforcement learning and transfer learning in wind speed and wind power forecasting modeling. Usually, wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state, which is usually achieved based on the state of the atmosphere that encompasses nearby atmospheric pressure, temperature, roughness, and obstacles. As an effective method of high-dimensional feature extraction, deep neural network can theoretically deal with arbitrary nonlinear transformation through proper structural design, such as adding noise to outputs, evolutionary learning used to optimize hidden layer weights, optimize the objective function so as to save information that can improve the output accuracy while filter out the irrelevant or less affected information for forecasting. The establishment of high-precision wind speed and wind power forecasting models is always a challenge due to the randomness, instantaneity and seasonal characteristics
NEW MODEL FOR SOLAR RADIATION ESTIMATION FROM MEASURED AIR TEMPERATURE AND RELATIVE HUMIDITY IN NIGERIA
Solar radiation prediction is essential for effective and reliable solar power project, predicted solar radiation can be used for accurate solar energy prediction. Solar radiation measurement is not sufficient in Nigeria for various reasons such as maintenance and repair cost, calibration of instrument, and expansive of measuring device. In this paper, adaptive neuro-fuzzy inference system (ANFIS) model was developed to predict the monthly average solar radiation in Nigeria. Air temperature of monthly mean minimum temperature, maximum temperature and relative humidity obtained from Nigerian Meteorological Agency (NIMET) were used as inputs to the ANFIS model and monthly mean global solar radiation was used as out of the model. Statistical evaluation of the model was done based on root mean square error (RMSE) and correlation coefficient R to examine the accuracy of the developed model. The values of RMSE and R for the training data are 0.91315MJ/m2 and 0.91264MJ/m2 respectively. The obtained result showed a good correlation between the predicted and measured solar radiation which proves ANFIS to be a good model for solar radiation prediction.  http://dx.doi.org/10.4314/njt.v36i3.3
Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks
Multivariate time series forecasting is an important machine learning problem
across many domains, including predictions of solar plant energy output,
electricity consumption, and traffic jam situation. Temporal data arise in
these real-world applications often involves a mixture of long-term and
short-term patterns, for which traditional approaches such as Autoregressive
models and Gaussian Process may fail. In this paper, we proposed a novel deep
learning framework, namely Long- and Short-term Time-series network (LSTNet),
to address this open challenge. LSTNet uses the Convolution Neural Network
(CNN) and the Recurrent Neural Network (RNN) to extract short-term local
dependency patterns among variables and to discover long-term patterns for time
series trends. Furthermore, we leverage traditional autoregressive model to
tackle the scale insensitive problem of the neural network model. In our
evaluation on real-world data with complex mixtures of repetitive patterns,
LSTNet achieved significant performance improvements over that of several
state-of-the-art baseline methods. All the data and experiment codes are
available online.Comment: Accepted by SIGIR 201
A hybrid PSO-ANFIS approach for horizontal solar radiation prediction in Nigeria
For efficient and reliable hydrogen production via solar photovoltaic system, it is important to obtain accurate solar radiation data. Though there are equipment specifically designed for solar radiation prediction but are very expensive and have high maintenance cost that most countries like Nigeria are unable to purchase. In this study, the accuracy of a hybrid PSO-ANFIS method is examined to predict horizontal solar radiation in Nigeria. The prediction is done based on the available meteorological data obtained from NIMET Nigeria. The meteorological data used for this study are monthly mean minimum temperature, maximum temperature, relative humidity and sunshine hours, which serves as inputs to the developed model. The model accuracy is evaluated using two statistical indicators Root Mean Square Error (RMSE) and Coefficient of determination (R²). The accuracy of the proposed model is validated using ANFIS, GA-ANFIS models and other literatures. Based on the statistical parameters used for the model evaluation, the results obtained proves PSO-ANFIS as a good model for predicting solar radiation with the values of RMSE=0.68318, R²=0.9065 at the training stage and RMSE=1.3838, R²=0.8058 at the testing stage. This proves the potentiality of PSO-ANFIS technique for accurate solar radiation prediction
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