The Solar Energy Forecasting by Pearson Correlation using Deep Learning Techniques (original) (raw)
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Solar irradiance is fluctuating and intermittent in nature. In order to optimally harness solar energy, this variability needs to be accounted for. Forecasting solar radiation proves to be helpful in optimal design, and operation of solarenergy based systems. This paper presents a solar irradiance forecasting scheme for multi-horizon forecasting of solar radiation considering 3/6/24 hours ahead scenarios. The proposed model uses long short term memory network, considering the dependence between hours of the same day along with other variables such as: direct horizontal irradiance, direct normal irradiance, relative humidity, dew point, temperature, wind speed, and wind direction. Solar radiations for four different locations of the Thar desert region have been forecasted. The model is optimized in terms of number of neurons and is evaluated using standard statistical indicators: RMSE and MAPE. RMSE for four different locations varied in the range of 0.099 to 0.181, along with MAPE values, which range from 6.79% to 10.47%. Low values of RMSE and MAPE indicate the efficacy of the proposed model.
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Solar Irradiance Forecasting using Deep Learning Approaches
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Reliable estimation of solar irradiance is required for many solar energy applications such as photovoltaics, water heating, cooking, solar microgrids, etc. Deep Learning techniques have shown outstanding behaviour for analysing complex datasets efficiently with high accuracy. Multi-Layer Perceptron (MLP), Long-Short Term Memory (LSTM), and Gated Recurrent Unit (RGU) techniques are found to be the most competitive techniques in the literature for solar irradiance forecasting. Therefore, in this study, a comparative analysis of those models is carried out using eleven years of NASA satellite data for training and testing. The grid search technique is used to optimize the networks architectures to ensure the best performance of the models for forecasting daily global solar irradiance. The results show that all models have similar accuracy with a mean square error close to 0.017 kWh/m2/day. However, the speed of training varies between 17 and 208 seconds for each model where GRU has shown higher speed than LSTM despite of containing more layers due to their computational complexity. The MLP is found to be the most efficient model due to using a low number of parameters 49,281 as compared to 1,025,793 for GRU. The study is of importance for reliable solar irradiance forecasting for any location worldwide. INDEX TERMS Solar energy, solar irradiance, forecasting, machine learning techniques, artificial neural network.
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Predicting solar irradiance has been an important topic in renewable energy generation. Prediction improves the planning and operation of photovoltaic systems and yields many economic advantages for electric utilities. The irradiance can be predicted using statistical methods such as artificial neural networks (ANN), support vector machines (SVM), or autoregressive moving average (ARMA). However, they either lack accuracy because they cannot capture long-term dependency or cannot be used with big data because of the scalability. This paper presents a method to predict the solar irradiance using deep neural networks. Deep recurrent neural networks (DRNNs) add complexity to the model without specifying what form the variation should take and allow the extraction of high-level features. The DRNN is used to predict the irradiance. The data utilized in this study is real data obtained from natural resources in Canada. The simulation of this method will be compared to several common methods such as support vector regression and feedforward neural networks (FNN). The results show that deep learning neural networks can outperform all other methods, as the performance tests indicate.
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2017 10th International Conference on Electrical and Electronics Engineering (ELECO), 2017
Time series forecasting is currently used in various areas. Energy management is also one of the most prevalent application areas. As a matter of fact, energy suppliers and managers have to face with the energy mix problem. Electricity can be produced from fossil fuels, from nuclear energy, from bio-fuels or from renewable energy resources. Concerning electricity generation system based on solar irradiation, it is very important to know precisely the amount of electricity available for the different sources and at different horizons: minutes, hours, and days. Depending on the horizon, two main classes of methods can be used to forecast the solar irradiation: statistical time series forecasting methods for short to midterm horizons and numerical weather prediction methods for medium to long-term horizons. On this paper we focus only on statistical time series forecasting methods. The aim of this study is to assess if deep learning can be suitable and competitive on the solar irradiat...
Journal of Solar Energy Research Updates, 2020
The mathematical models used in the estimation of GHI on the Earth's surface are inconvenient because they always assume that the sky clarity index is constant. Hence, these models are often confronted with long-period empirical ground measurements that may exceeds 11 years. The impact of cloud cover on an electric power generation site is a very critical parameter for installing a solar power plant and evaluating its productivity. The state of knowledge about the sun influence, the greenhouse effect on climate change, and cloud occurrence can’t be described in a mathematical or numerical model. Therefore, in this paper, we propose the use of Deep-Learning techniques to predict any site’s productivity by analyzing its potential insolation. We also suggest the analysis of the ground and satellite- based measurements collected over 30 years. We propose the estimation of future climate change affecting cloud cover.
A Flexible and Robust Deep Learning-Based System for Solar Irradiance Forecasting
IEEE Access, 2021
Most studies about the solar forecasting topic do not analyze and exploit the temporal and spatial components that are inherent to such a task. Furthermore, they mostly focus just on precision and not on other meaningful features, such as flexibility and robustness. With the current energy production trends, where many solar panels are distributed across city rooftops, there is a need to manage all this information simultaneously and to be able to add and remove sensors as needed. Likewise, robust models need to be able to cope with (inevitable) sensor failure and continue producing reliable predictions. Due to all of this, solar forecasting models need to be as decoupled as possible from the number of data sources that feed them and their geographical distribution, enabling also the reusability of the models. This article contributes with a family of Deep Learning models for solar irradiance forecasting complying with the aforementioned features, i.e. flexibility and robustness. In the first stage, several Artificial Neural Networks are trained as a basis for predicting solar irradiance on several locations at the same time. Thereupon, a family of models that work with irradiance maps thanks to Convolutional Long Short-Term Memory layers is presented, obtaining forecast skills between 7.4% and 41% (depending on the location and horizon) compared to the baseline. The latter family comes with flexibility and robustness features, which are required in large-scale Intelligent Environments, such as Smart Cities. Working with irradiance maps means that new sensors can be added (or removed) as needed, without requiring rebuilding the model. Experiments carried out show that sensor failures have a mild impact on the prediction error for several forecast horizons. INDEX TERMS Convolutional long short-term memory (Conv-LSTM), deep learning, irradiance map, solar irradiance, time series forecasting.