Designing a long short-term network for short-term forecasting of global horizontal irradiance

Forecasting of solar radiation for a cleaner environment using robust machine learning techniques

Intensified research is going on worldwide to increase renewable energy sources like solar and wind to reduce emissions and achieve worldwide targets and also to address the depleting fossil fuels resources and meet the increasing energy demand of the population. Solar radiation (SR) is intermittent, so forecasting solar radiation is a must. The objective of this research is to use modern machine techniques for different climatic conditions to forecast SR with higher accuracy. The required dataset is collected from National Solar Radiation Database having features such as temperature, pressure, relative humidity, dew point, solar zenith angle, wind speed, and direction, concerning the y-parameter Global Horizontal Irradiance (GHI) (W/m²). The collected data is first split based on different types of climatic conditions. Each climatic model was trained on various machine learning (ML) algorithms like multiple linear regression (MLR), support vector regression (SVR), decision tree regression (DTR), random forest regression (RFR), gradient boosting regression (GBR), lasso and ridge regression, and deep learning algorithm especially long-short-term memory (LSTM) using Google Colab Platform. From the analysis, LSTM has the least error approximation of 0.0040 loss at the 100^th epoch and of all ML models, gradient boosting and RFR top high, when it comes to the Hot weather season-gradient boosting leads 2% than RFR, and similarly for cold weather, autumn and monsoon climate-RFR has 1% higher accuracy than gradient boosting. This high-accuracy model is deployed in a user interface (UI) that will be more useful for real-time solar prediction, load operators for maintenance scheduling, stock commitment, and load dispatch centres for engineers to decide on setting up solar panels, for household clients and future researchers.

Hourly solar irradiation forecast using hybrid local gravitational clustering and group method of data handling methods

The foundation for many solar energy uses as well as economic and environmental concerns is global solar irradiation information. However, due to solar irradiation and measurement variations, reliable worldwide statistics on solar irradiation are frequently impossible or challenging to acquire. In addition, more precise forecasts of solar irradiation play an increasingly important role in electric energy planning and management due to integrating photovoltaic solar systems into power networks. Hence, this paper proposes a new hybrid model for 1-h ahead solar irradiation forecasting called LGC-GMDH (local gravitational clustering-group method of data handling). The novel LGC-GMDH model is based on local clustering that adequately captures the underlying features of the solar irradiation time series. Each cluster is then forecasted using the GMDH method, which is a self-organized system capable of handling very complicated nonlinear problems. Finally, these local forecasts are reconstructed in order to obtain the global forecast. Comparative study between the proposed model and the traditional individual models such as backpropagation neural network (BP), supporting vector machines (SVM), long short-term memory (LTSM), and hybrid models such as BP-MLP, RNN-MLP, LSTM-MLP hybrid wavelet packet decomposition (WPD), convolutional neural network (CNN) with LSTM-MLP, and ANFIS clustering shows that the proposed model overcomes conventional model deficiencies and achieves more precise predicting outcome.

Generative Adversarial Network to evaluate quantity of information in financial markets

Nowadays, the information obtainable from the markets are potentially limitless. Economic theory has always supported the possible advantage obtainable from having more information than competitors, however quantifying the advantage that these can give has always been a problem. In particular, in this paper we study the amount of information obtainable from the markets taking into account only the time series of the prices, through the use of a specific Generative Adversarial Network. We consider two types of financial instruments traded on the market, stocks and cryptocurrencies: the first are traded in a market subject to opening and closing hours, whereas cryptocurrencies are traded in a 24/7 market. Our goal is to use this GAN to be able to “convert” the amount of information that the different instruments can have in discriminative and predictive power, useful to improve forecast. Finally, we demonstrate that by using the initial dataset with the 5 most important feature useds by traders, the prices of cryptocurrencies present higher discriminatory and predictive power than stocks, while by adding a feature the situation can be completely reversed.

A Novel Feature Representation for Prediction of Global Horizontal Irradiance Using a Bidirectional Model

Complex weather conditions—in particular clouds—leads to uncertainty in photovoltaic (PV) systems, which makes solar energy prediction very difficult. Currently, in the renewable energy domain, deep-learning-based sequence models have reported better results compared to state-of-the-art machine-learning models. There are quite a few choices of deep-learning architectures, among which Bidirectional Gated Recurrent Unit (BGRU) has apparently not been used earlier in the solar energy domain. In this paper, BGRU was used with a new augmented and bidirectional feature representation. The used BGRU network is more generalized as it can handle unequal lengths of forward and backward context. The proposed model produced 59.21%, 37.47%, and 76.80% better prediction accuracy compared to traditional sequence-based, bidirectional models, and some of the established states-of-the-art models. The testbed considered for evaluation of the model is far more comprehensive and reliable considering the variability in the climatic zones and seasons, as compared to some of the recent studies in India.

Comparative Study of Univariate and Multivariate Long Short-Term Memory for Very Short-Term Forecasting of Global Horizontal Irradiance

Accurate global horizontal irradiance (GHI) forecasting is crucial for efficient management and forecasting of the output power of photovoltaic power plants. However, developing a reliable GHI forecasting model is challenging because GHI varies over time, and its variation is affected by changes in weather patterns. Recently, the long short-term memory (LSTM) deep learning network has become a powerful tool for modeling complex time series problems. This work aims to develop and compare univariate and several multivariate LSTM models that can predict GHI in Guntur, India on a very short-term basis. To build the multivariate time series models, we considered all possible combinations of temperature, humidity, and wind direction variables along with GHI as inputs and developed seven multivariate models, while in the univariate model, we considered only GHI variability. We collected the meteorological data for Guntur from 1 January 2016 to 31 December 2016 and built 12 datasets, each containing variability of GHI, temperature, humidity, and wind direction of a month. We then constructed the models, each of which measures up to 2 h ahead of forecasting of GHI. Finally, to measure the symmetry among the models, we evaluated the performances of the prediction models using root mean square error (RMSE) and mean absolute error (MAE). The results indicate that, compared to the univariate method, each multivariate LSTM performs better in the very short-term GHI prediction task. Moreover, among the multivariate LSTM models, the model that incorporates the temperature variable with GHI as input has outweighed others, achieving average RMSE values 0.74 W/m2–1.5 W/m2.