Comparison of short-term solar irradiance forecasting methods when weather conditions are complicated

Yu, Yunjun; Cao, Junfei; Wan, Xiaofeng; Zeng, Fanpeng; Jianbo, Xin; Ji, Qingzhao

doi:10.1063/1.5041905

Cited by 13 publications

(6 citation statements)

References 21 publications

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“…The distributed photovoltaic system has the characteristics of intermittent, random and volatility, which is reflected in the distribution of solar irradiance in different seasons and weathers. Yu et al [26] demonstrated in previous work that RNN has better prediction performance than BPNN and RBFNN in sunny, rainy and cloudy days. LSTM, as the deep structure of RNN, is a solution for vanishing gradient and exploding gradient caused due to its special hidden layer cell structure design, which allowed the RNN models with LSTM units to model both short and long term temporal dependencies in time-series data.…”

Section: Discussionmentioning

confidence: 99%

“…An RNN is considered to be an effective tool for time-series data prediction. Yu et al [26] demonstrated in previous work that RNN has better prediction performance than backpropagation NN (BPNN) and radial basis function NN (RBFNN) in sunny, rainy and cloudy days. To improve the prediction accuracy, more variables and longer time series need to be added.…”

Section: The Incorporation Of Unstablementioning

confidence: 99%

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An LSTM Short-Term Solar Irradiance Forecasting Under Complicated Weather Conditions

2019

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Complicated weather conditions lead to intermittent, random and volatility in photovoltaic (PV) systems, which makes PV predictions difficult. A recurrent neural network (RNN) is considered to be an effective tool for time-series data prediction. However, when the weather changes intensely, the long-term sequence of multivariate may cause gradient vanishing (exploding) during the training of RNN, leading the prediction results to local optimum. Long short-term memory (LSTM) network is the deep structure of RNN. Due to its special hidden layer unit structure, it can preserve the trend information contained in the longterm sequence, which is allowed to solve the problems of RNN and improve performance. An LSTM-based approach is applied for short-term predictions in this study based on a timescale that encompasses global horizontal irradiance (GHI) one hour in advance and one day in advance. Inaccurate forecasts usually occur on cloudy days, and the results of ANN and SVR in the literature prove this. To improve prediction accuracy on cloudy days, the clearness-index was introduced as an input data for the LSTM model and to classify the type of weather by k-means during the data processing, where cloudy days are classified as the cloudy and the mixed(partially cloudy). NN models are established to compare the accuracy of different approaches and the cross-regional study is to prove whether the method can be generalizable. From the results of hourly forecast, the R 2 coefficient of LSTM on cloudy days and mixed days is exceeding 0.9, while the R 2 of RNN is only 0.70 and 0.79 in Atlanta and Hawaii. From the results of daily forecast, All R 2 on cloudy days is about 0.85. However, the LSTM is still very effective in improving of RNN and more accurate than other models.

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Section: Discussionmentioning

confidence: 99%

Section: The Incorporation Of Unstablementioning

confidence: 99%

An LSTM Short-Term Solar Irradiance Forecasting Under Complicated Weather Conditions

2019

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“…Furthermore, the performance of forecast model has been enhanced by ensembling the extracted optimized features of different models. Yu et al analysed Elman NN (ENN), Radial Basis Function (RBF), and backpropagation NN (BNN) approaches on per hour forecasting horizon with different meteorological parameters. To evaluate the performance under different weather condition, data samples have been taken from different seasons of the year.…”

Section: Solar Forecastingmentioning

confidence: 99%

A review on solar forecasting and power management approaches for energy‐harvesting wireless sensor networks

Kakkar

2020

Int J Communication

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Summary For rechargeable wireless sensor nodes, effective power management is of prime importance because of the stochastic behaviour of the environmental resources. A key issue in integrating solar resources with wireless sensor networks (WSNs) is the need of precise irradiance measurements and power to resource modelling. WSNs are employed in an adhoc manner comprises of numerous sensing nodes and organised as a network for the sake of checking and balancing the environmental factors. Each node has sensing, computation, communication, and locomotion capabilities but operates with limited battery life. Energy harvesting is a way of powering these WSNs by harvesting energy from the environment. By considering harvested energy as an energy source, certain considerations are different from that of battery‐operated networks. Nondeterministic energy availability with respect to time is the reason behind these differences, which put a limit on the maximum rate at which energy can be used. Thus, reliable knowledge of solar radiation is essential for informed design, deployment planning, and optimal management of energy in rechargeable WSNs. Further, power management is essential in self‐powerssed networks to efficiently utilize the available energy. In this paper, a detailed survey on different solar forecasting techniques has been presented for precise energy estimates. A detailed study on energy efficient power management techniques is also proposed to address the feasibility of energy‐harvesting approach in WSNs.

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“…Photovoltaic (PV) panels are the biggest driving force in the rapid growth of solar power generation [2]. However, PV systems are susceptible to environmental factors such as sunlight, season, and geographic location, resulting in the PV characteristics of being stochastic, intermittent, and variable [3], [4]. These characteristics are required to reduce the efficiency of PV output [5].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Solar Irradiance One Hour Ahead Based on Quantum Long Short-Term Memory Network

Liu

et al. 2023

IEEE Trans. Quantum Eng.

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The short-term forecasting of photovoltaic (PV) power generation ensures the scheduling and dispatching of electrical power, helps design a PV-integrated energy management system, and enhances the security of grid operation. However, due to the randomness of solar energy, the output of the PV system will fluctuate, which will affect the safe operation of the grid. To solve this problem, a high-precision hybrid prediction model based on variational quantum circuit (VQC) and long short-term memory (LSTM) network is developed to predict solar irradiance 1 hour in advance. VQC is embedded in LSTM to iteratively optimize the weight parameters of four gates (forgetting gate, input gate, cell state, and output gate) to improve prediction accuracy. To evaluate the prediction performance of this model, five solar radiation observatories located in China are selected, together with widely used models including seasonal autoregressive integrated moving average, convolution neural network, recurrent neural network (RNN), gate recurrent unit, (GRU), and LSTM; comparisons are made under different seasons and months. The experimental results show that the annual average root mean square error of the quantum long short-term memory model is 61.756 W/m 2 , which is reduced by 10.7%, 13.9%, 8.1%, 3.8%, and 3.4%, respectively, compared with other models; the annual average mean absolute error is 24.257 W/m 2 , which is reduced by 28.1%, 28.9%, 24.1%, 12.2%, and 12.8%, respectively, compared with other models; the annual average R-Square (R 2 ) is 0.946, which is improved by 1.5%, 1.9%, 1.2%, 0.4%, and 0.4%, respectively, compared with other models. INDEX TERMSLong short-term memory (LSTM) network, quantum neural network, solar irradiance forecasting, variational quantum circuit (VQC). Engineering uantum Transactions on IEEE Yu et al.: PREDICTION OF SOLAR IRRADIANCE ONE HOUR AHEAD BASED ON QUANTUM LSTM NETWORK

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Comparison of short-term solar irradiance forecasting methods when weather conditions are complicated

Cited by 13 publications

References 21 publications

An LSTM Short-Term Solar Irradiance Forecasting Under Complicated Weather Conditions

An LSTM Short-Term Solar Irradiance Forecasting Under Complicated Weather Conditions

A review on solar forecasting and power management approaches for energy‐harvesting wireless sensor networks

Prediction of Solar Irradiance One Hour Ahead Based on Quantum Long Short-Term Memory Network

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