Photovoltaic yield prediction using an irradiance forecast model based on multiple neural networks

Durrani, Saad Parvaiz; Balluff, Stefan; Wurzer, Lukas; Krauter, Stefan

doi:10.1007/s40565-018-0393-5

Cited by 62 publications

(26 citation statements)

References 20 publications

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“…Leva et al [22] measured the correlation between weather and PV power and introduced a clearness-index to divide the sky state into clear, cloudy and partially cloudy conditions to guide the supervised learning of the ANN and to classify the prediction results to evaluate. Durrani et al [23] and Alfadda et al [24] established models of multi-layer perceptron (MLP). Forecasting results of the above three all showed that ANN and MLP cannot solve the problem of unstable PV power in cloudy conditions.…”

Section: The Incorporation Of Unstablementioning

confidence: 99%

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: The Incorporation Of Unstablementioning

confidence: 99%

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

2019

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“…Saad P. et.al. [2], worked on photovoltaic yield prediction using an irradiance forecast model based on multiple neural networks. The proposed irradiance forecast model is based on multiple feed-forward neural networks.…”

Section: Literature Surveymentioning

confidence: 99%

IOT based solar energy prophecy using RNN architecture

Raju¹,

Raju²,

Shailaja³

et al. 2020

E3S Web Conf.

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It is the 21st century and scientists say that by the end of this century, resources will be replenished and the only way the future generations can access energy is through renewable resources— those which are inexhaustible. One such source is sunlight, which has a guaranteed stay in the long run. The energy thus given is termed as solar energy. In the present paper it is tried to solve the issue of limited resources and their adverse effects. Since the power generated from solar energy systems is highly variable, due to its dependence on meteorological conditions, an efficient method of usage of this fluctuating but precious energy source has to come in picture. This requires the scope of reliable forecast information as the development of predictive control algorithms for efficient energy management and monitoring for residential grid connected photovoltaic systems. The paper has given an overview of different applications and models for solar irradiance and photovoltaic power prediction, including time series models based on live measured data from rooftop solar power plant located at 17.5203° N, 78.3674° E. For experimentation, data collected over four years from the solar power plant was used in order to the train machine and understand the characteristics of the solar power plant and gives the predicted energy as the result. The use of Deep Learning is done where LSTM is used for the training and keras and tensorflow are used for obtaining the result. The mean square error thus obtained is 0.015.

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“…As an intermediate entity between the end users and the power system operator, an aggregator (Agg) has been proposed to manage the energy of local RESs and DERs [2], and will play an important role in future smart grids. However, the power outputs of RESs have uncertain characteristics [3], [4], causing challenges in the energy management of the Agg. Employing the flexibility of electric vehicles (EVs) is widely considered as an economical and efficient solution to the problem [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Multi-time Scale Energy Management Strategy of Aggregator Characterized by Photovoltaic Generation and Electric Vehicles

Zhou

Yang

et al. 2020

Journal of Modern Power Systems and Clean Energy

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The increasing number of photovoltaic (PV) generation and electric vehicles (EVs) on the load side has necessitated an aggregator (Agg) in power system operation. In this paper, an Agg is used to manage the energy profiles of PV generation and EVs. However, the daily management of the Agg is challenged by uncertain PV fluctuations. To address this problem, a robust multi-time scale energy management strategy for the Agg is proposed. In a day-ahead phase, robust optimization is developed to determine the power schedule. In a real-time phase, a rolling horizon-based convex optimization model is established to track the day-ahead power schedule based on the flexibilities of the EVs. A case study indicates a good scheduling performance under an uncertain PV output. Through the convexification, the solving efficiency of the real-time operation model is improved, and the overcharging and over-discharging problems of EVs can be suppressed to a certain extent. Moreover, the power deviation between day-ahead and real-time scheduling is controllable when the EV dispatching capacity is sufficient. The strategy can ensure the flexibility of the Agg for real-time operation.

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Photovoltaic yield prediction using an irradiance forecast model based on multiple neural networks

Cited by 62 publications

References 20 publications

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

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

IOT based solar energy prophecy using RNN architecture

Multi-time Scale Energy Management Strategy of Aggregator Characterized by Photovoltaic Generation and Electric Vehicles

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