A Graph Neural Network based Deep Learning Predictor for Spatio-Temporal Group Solar Irradiance Forecasting

2021 ◽  
pp. 1-1
Author(s):  
Xuan Jiao ◽  
Xingshuo Li ◽  
Dingyi Lin ◽  
Weidong Xiao
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Solar Irradiance ◽  
Temporal Group ◽  
Spatio Temporal

scholarly journals Improving Lives of Indebted Farmers Using Deep Learning: Predicting Agricultural Produce Prices Using Convolutional Neural Networks

2020 ◽  
Vol 34 (08) ◽  
pp. 13294-13299
Author(s):  
Hangzhi Guo ◽  
Alexander Woodruff ◽  
Amulya Yadav
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Network Architecture ◽  
Prediction Models ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Machine Learning Algorithms ◽  
Volume Data ◽  
Farmer Suicides ◽  
Spatio Temporal

Farmer suicides have become an urgent social problem which governments around the world are trying hard to solve. Most farmers are driven to suicide due to an inability to sell their produce at desired profit levels, which is caused by the widespread uncertainty/fluctuation in produce prices resulting from varying market conditions. To prevent farmer suicides, this paper takes the first step towards resolving the issue of produce price uncertainty by presenting PECAD, a deep learning algorithm for accurate prediction of future produce prices based on past pricing and volume patterns. While previous work presents machine learning algorithms for prediction of produce prices, they suffer from two limitations: (i) they do not explicitly consider the spatio-temporal dependence of future prices on past data; and as a result, (ii) they rely on classical ML prediction models which often perform poorly when applied to spatio-temporal datasets. PECAD addresses these limitations via three major contributions: (i) we gather real-world daily price and (produced) volume data of different crops over a period of 11 years from an official Indian government administered website; (ii) we pre-process this raw dataset via state-of-the-art imputation techniques to account for missing data entries; and (iii) PECAD proposes a novel wide and deep neural network architecture which consists of two separate convolutional neural network models (trained for pricing and volume data respectively). Our simulation results show that PECAD outperforms existing state-of-the-art baseline methods by achieving significantly lesser root mean squared error (RMSE) - PECAD achieves ∼25% lesser coefficient of variance than state-of-the-art baselines. Our work is done in collaboration with a non-profit agency that works on preventing farmer suicides in the Indian state of Jharkhand, and PECAD is currently being reviewed by them for potential deployment.

Probabilistic and Point Solar Forecasting Using Attention Based Dilated Convolutional Neural Network

2020 ◽  
Author(s):  
Moumita Saha ◽  
Bhalchandra Naik ◽  
Claire Monteleoni
Keyword(s):  
Neural Network ◽  
Climate Change ◽  
Deep Learning ◽  
Solar Irradiance ◽  
Weather Prediction ◽  
Clean Energy ◽  
Solar Forecasting ◽  
Short Term ◽  
Term Forecast

<p>Climate change is evident at present with threatening effects as intense hurricanes, rising sea level, increase number of droughts, and shifting weather patterns. Burning of fossil fuels and anthropogenic activities increase the greenhouse gases concentration in atmosphere, which is a major cause behind the climate change. Renewable energy as solar is a good source for combating the causes of climate change by producing clean energy.  </p><p>The efficient integration of solar energy into electrical grids requires an accurate prediction of solar irradiance. The solar irradiance is the flux of radiant energy received per unit area of the earth from the sun. Existing techniques use basic stochastic (such Gaussian model, hidden Markov model, etc.) and ensemble neural network models for solar forecasting. However, recent literature reflects the potential of deep-learning models over the statistical model.</p><p>In this paper, we propose a deep-learning-based one-dimensional, multi-quantile convolution neural network for predicting the solar irradiance. The network employs dilation in its convolution kernel, which helps capturing the long-term dependencies between instances of the input climatic variables. Additionally, we also incorporate the attention mechanism between the input and learned representation from the convolution, which allows attending to the temporal instance of features for improved prediction. We perform both short-term (three hours ahead) and long-term (twenty-four hours ahead) solar irradiance prediction. We exhaustively present the forecast for all four seasons (spring, summer, fall, and winter) as well as for the whole year. We provide a point solar forecast along with forecast at different quantiles. Quantile forecast provides a range of estimates with varying confidence intervals, which allows better interpretation as compared to point forecast. This notion of confidence associated with each quantile makes the forecasting probabilistic.</p><p>In order to validate our approach, we consider two cities (Boulder and Fort Peck) from the SURFAD network and examine twenty climatic features as input to our model.  Additionally, we learned embedded reduced input dimension using an autoencoder. The proposed architecture is trained with all the input features and reduced features, independently. We observe the prediction error for Boulder is higher than Fort Peck, which can be due to the volatile weather of Boulder. The proposed model forecasts the solar irradiance for winter with a higher accuracy as compared to spring, summer, or fall. We observe the correlation coefficients as 0.90 (Boulder) and 0.92 (Fort Peck) between the actual and predicted solar irradiance.  The long-term forecast shows average improvements of 37.1% and 33.1% in root mean square error (RMSE) over existing numerical weather prediction model for Boulder and Fort Peck, respectively. Similarly, the short-term forecast shows improvements of 33.7% and 34.2% for the respective cities.</p>

Forecasting Model of Short-term PM2.5 Concentration based on Deep Learning

2020 ◽  
Author(s):  
Wei Tang ◽  
Wen-fang Zhao ◽  
Runsheng Lin ◽  
Yong Zhou
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sequence Data ◽  
Impact Factors ◽  
Support Vector ◽  
Observation Data ◽  
Short Term ◽  
Concentration Data ◽  
Spatio Temporal ◽  
Pm2.5 Concentration

<p>In order to improve the accuracy of PM2.5 concentration forecast in Beijing Meteorological Bureau, a deep learning prediction model based on convolutional neural network (CNN) and long short term memory neural network (LSTM) was proposed. Firstly, the feature vectors extraction was carried out by using the correlation analysis technique from meteorological data such as temperature, wind, relative humidity, precipitation, visibility and atmospheric pressure. Secondly, taking into account the fact that PM2.5 concentration was significantly affected by surrounding meteorological impact factors, meteorological grid analysis data was novel involved into the model, as well as the historical PM2.5 concentration data and meteorological observation data of the present station. Spatio-temporal sequence data was generated from these data after integrated processing. High level spatio-temporal features were extracted through the combination of the CNN and LSTM. Finally, future 24-hour prediction of PM2.5 concentration was made by the model. The comparison among the accuracy of this optimized model, support vector machine (SVM) and existing PM2.5 forecast system is performed to evaluate their performance. The results show that the proposed CNN-LSTM model performs better than SVM and current operational models in Beijing Meteorological Bureau, which has effectively improved the prediction accuracy of PM2.5 concentration for different time predictions scales in the next 24 hours.</p>

Convolutional Graph Autoencoder: A Generative Deep Neural Network for Probabilistic Spatio-Temporal Solar Irradiance Forecasting

2020 ◽  
Vol 11 (2) ◽  
pp. 571-583 ◽  
Author(s):  
Mahdi Khodayar ◽  
Saeed Mohammadi ◽  
Mohammad E. Khodayar ◽  
Jianhui Wang ◽  
Guangyi Liu
Keyword(s):  
Neural Network ◽  
Solar Irradiance ◽  
Deep Neural Network ◽  
Spatio Temporal

scholarly journals Short-Term Solar Irradiance Forecasting Based on a Hybrid Deep Learning Methodology

Information ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 32 ◽  
Author(s):  
Ke Yan ◽  
Hengle Shen ◽  
Lei Wang ◽  
Huiming Zhou ◽  
Meiling Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Solar Irradiance ◽  
Power Plants ◽  
Short Term Memory ◽  
Learning Model ◽  
Weather Data ◽  
Short Term ◽  
Original Dataset ◽  
Deep Learning Model

Accurate prediction of solar irradiance is beneficial in reducing energy waste associated with photovoltaic power plants, preventing system damage caused by the severe fluctuation of solar irradiance, and stationarizing the power output integration between different power grids. Considering the randomness and multiple dimension of weather data, a hybrid deep learning model that combines a gated recurrent unit (GRU) neural network and an attention mechanism is proposed forecasting the solar irradiance changes in four different seasons. In the first step, the Inception neural network and ResNet are designed to extract features from the original dataset. Secondly, the extracted features are inputted into the recurrent neural network (RNN) network for model training. Experimental results show that the proposed hybrid deep learning model accurately predicts solar irradiance changes in a short-term manner. In addition, the forecasting performance of the model is better than traditional deep learning models (such as long short term memory and GRU).

scholarly journals A Review on Deep Learning Models for Forecasting Time Series Data of Solar Irradiance and Photovoltaic Power

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6623
Author(s):  
Rial A. Rajagukguk ◽  
Raden A. A. Ramadhan ◽  
Hyun-Jin Lee
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Deep Learning ◽  
Hybrid Model ◽  
Solar Irradiance ◽  
Time Series Data ◽  
Series Data ◽  
Significant Finding ◽  
Learning Models ◽  
Evaluation Metric

Presently, deep learning models are an alternative solution for predicting solar energy because of their accuracy. The present study reviews deep learning models for handling time-series data to predict solar irradiance and photovoltaic (PV) power. We selected three standalone models and one hybrid model for the discussion, namely, recurrent neural network (RNN), long short-term memory (LSTM), gated recurrent unit (GRU), and convolutional neural network-LSTM (CNN–LSTM). The selected models were compared based on the accuracy, input data, forecasting horizon, type of season and weather, and training time. The performance analysis shows that these models have their strengths and limitations in different conditions. Generally, for standalone models, LSTM shows the best performance regarding the root-mean-square error evaluation metric (RMSE). On the other hand, the hybrid model (CNN–LSTM) outperforms the three standalone models, although it requires longer training data time. The most significant finding is that the deep learning models of interest are more suitable for predicting solar irradiance and PV power than other conventional machine learning models. Additionally, we recommend using the relative RMSE as the representative evaluation metric to facilitate accuracy comparison between studies.

Sign in / Sign up

Export Citation Format

Share Document