Deep learning based long-term global solar irradiance and temperature forecasting using time series with multi-step multivariate output

Azizi, Narjes; Yaghoubirad, Maryam; Farajollahi, Meisam; Ahmadi, Abolfazl

doi:10.1016/j.renene.2023.01.102

Cited by 32 publications

(9 citation statements)

References 50 publications

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“…Temperature variables are among the most popular, featured in recent publications like [88] , [89] , [90] , [91] , [75] , due to their easy accessibility and high Pearson correlation coefficients, which can reach 0.9. Similarly, wind speed variables, as implemented in [92] , [81] , [77] , and humidity [81] , [93] , which have moderate Pearson correlation coefficients, are also utilized. Time-related variables, presented in [94] , [95] , such as day of the year, datetime, and trigonometric representations of time, are frequently used to extract insights from the cyclic behavior of solar radiation produced by the sun.…”

Section: State Of the Artmentioning

confidence: 99%

“…While this fine granularity provides highly accurate information for modeling, it significantly increases computational demands due to the large volume of data. On the other end of the spectrum, monthly data granularity is the longest resolution, noted in two publications [96] , [93] . This granularity is well-suited for long-term predictions but necessitates extensive datasets for effective forecasting.…”

Section: State Of the Artmentioning

confidence: 99%

See 1 more Smart Citation

Towards energy efficiency: A comprehensive review of deep learning-based photovoltaic power forecasting strategies

Husein,

Gago,

Hasan

et al. 2024

Heliyon

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Section: State Of the Artmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

Towards energy efficiency: A comprehensive review of deep learning-based photovoltaic power forecasting strategies

Husein,

Gago,

Hasan

et al. 2024

Heliyon

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“…The combination of deep learning solutions and traditional mining engineering problems provides a scientifically effective method and means for the design of coal pillar widths [17,18]. The LSTM network model has a good predictive performance on non-time series data, with special gating units and decent non-linear mapping capabilities [19][20][21]. It could overcome the long-term dependence on data characteristics in forecasting research.…”

Section: Introductionmentioning

confidence: 99%

Construction and Application of an Intelligent Prediction Model for the Coal Pillar Width of a Fully Mechanized Caving Face Based on the Fusion of Multiple Physical Parameters

Yan,

Wang,

et al. 2024

Sustainability

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The scientific and reasonable width of coal pillars is of great significance to ensure safe and sustainable mining in the western mining area of China. To achieve a precise analysis of the reasonable width of coal pillars in fully mechanized caving face sections of gently inclined coal seams in western China, this paper analyzes and studies various factors that affect the retention of coal pillars in the section, and calculates the correlation coefficients between these influencing factors. We selected parameters with good universality and established a data set of gently inclined coal seams based on 106 collected engineering cases. We used the LSTM algorithm loaded with a simulated annealing algorithm for training, and constructed a coal pillar width prediction model. Compared with other prediction algorithms such as the original LSTM algorithm, the residual sum of squares and root mean square error were reduced by 27.2% and 24.2%, respectively, and the correlation coefficient was increased by 12.6%. An engineering case analysis was conducted using the W1123 working face of the Kuangou Coal Mine. The engineering verification showed that the SA-CNN-LSTM coal pillar width prediction model established in this paper has good stability and accuracy for multi-parameter nonlinear coupling prediction results. We have established an effective solution for achieving the accurate reservation of coal pillar widths in the fully mechanized caving faces of gently inclined coal seams.

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“…MLP, LSTM, GRU, CNN, and CNN-LSTM models are used to forecast solar radiation and temperature for the next 10 years (1984-2021) using monthly data. CNN outperforms other models with four input parameters: global horizontal irradiance, temperature, surface pressure, relative humidity (RH), and two output parameters: temperature and radiation [24]. To address the intermittent and stochastic character of photovoltaic (PV) power generation, [25] provides an ultra-short-term prediction technique employing a convolutional neural network (CNN) and long short-term memory (LSTM) hybrid model powered by empirical wavelet transform (EWT).…”

Section: Introductionmentioning

confidence: 99%

Novel and Efficient Hybrid Deep Learning Approach for Solar Photovoltaic Power Forecasting Considering Meteorological Parameters

Aman,

Rizwan,

Kumar

2023

Preprint

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The power generation from photovoltaic plants depends on varying meteorological conditions. These meteorological conditions such as solar irradiance, temperature, and wind speed, are non-linear and stochastic thus affect estimation of photovoltaic power. Accurate estimation of photovoltaic power is essential for enhancing the functioning of solar power installations. The paper aims to develop a novel deep learning based photovoltaic power forecasting model on different weather conditions. The proposed model utilizes a two-stage deep learning framework for accurate solar power forecasting, which combines the long short-term memory (LSTM) and convolutional neural network (CNN) deep learning architectures. The key role of CNN layer is to identify the weather conditions, i.e., sunny, cloudy and rainy while the LSTM layer learns the patterns of solar power generation that depend on weather variations to estimate photovoltaic power. The proposed hybrid models consider meteorological factors, such as wind speed, sun irradiations, temperature, and humidity, including cloud cover and UV index to provide precise solar power forecasting. The presented hybrid model, a Root Mean Square Error of 0.0254, 0.03465 and 0.0824, Mean Square Error of 0.000645, 0.00120 and 0.00679, R2 of 0.9898, 0.9872 and 0.9358, Mean Average Error of 0.0163 and 0.0236 and 0.2521 for sunny, cloudy and rainy weather conditions respectively. The results demonstrate that presented deep learning based novel solar photovoltaic (SPV) power forecasting model can accurately forecast solar power based on instantaneous changes in generated power patterns, and aid in the optimization of PV power plant operations. The paper presents an effective methodology for forecasting solar power that can contribute to the improvement of solar power generation and management.

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Deep learning based long-term global solar irradiance and temperature forecasting using time series with multi-step multivariate output

Cited by 32 publications

References 50 publications

Towards energy efficiency: A comprehensive review of deep learning-based photovoltaic power forecasting strategies

Towards energy efficiency: A comprehensive review of deep learning-based photovoltaic power forecasting strategies

Construction and Application of an Intelligent Prediction Model for the Coal Pillar Width of a Fully Mechanized Caving Face Based on the Fusion of Multiple Physical Parameters

Novel and Efficient Hybrid Deep Learning Approach for Solar Photovoltaic Power Forecasting Considering Meteorological Parameters

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