A Hybrid Intelligent System to forecast solar energy production

Basurto, Nuño; Arroyo, Ángel; Vega, Rafael Vega; Quintián, Héctor; Calvo-Rolle, José Luís; Herrero, Álvaro

doi:10.1016/j.compeleceng.2019.07.023

Cited by 22 publications

(7 citation statements)

References 21 publications

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“…This aids in the integration of power grids with RES 11 . For successful grid operation, energy management, and economic scheduling, the need for an optimal solar photovoltaic (PV) power prediction technique becomes critical 12,13 …”

Section: Introductionmentioning

confidence: 99%

“…Short‐term forecasts may be adequate for primitive standalone or small PV systems, but long‐term forecasts are required for the operation of modern grid‐integrated PV systems. Hence, there is an immediate requirement for a much enhanced and reliable technique as the renewable power network becomes increasingly complex in structure 12–16 …”

Section: Introductionmentioning

confidence: 99%

“…11 For successful grid operation, energy management, and economic scheduling, the need for an optimal solar photovoltaic (PV) power prediction technique becomes critical. 12,13 The salient prediction techniques like autoregressive integrated moving average (ARIMA)/seasonal autoregressive integrated moving average (SARIMA; statistical), numerical weather prediction (NWP; physical), artificial neural network (ANN; machine learning), and hybrid methods have shown modest results in their tenure. However, they can only be applied for short-term forecasts.…”

Section: Introductionmentioning

confidence: 99%

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A novel long term solar photovoltaic power forecasting approach using LSTM with Nadam optimizer: A case study of India

Sharma

Soni

Paliwal

et al. 2022

Energy Science & Engineering

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Solar photovoltaic (PV) power is emerging as one of the most viable renewable energy sources. The recent enhancements in the integration of renewable energy sources into the power grid create a dire need for reliable solar power forecasting techniques. In this paper, a new long-term solar PV power forecasting approach using long short-term memory (LSTM) model with Nadam optimizer is presented. The LSTM model performs better with the time-series data as it persists information of more time steps. The experimental models are realized on a 250.25 kW installed capacity solar PV power system located at MANIT Bhopal, Madhya Pradesh, India. The proposed model is compared with two time-series models and eight neural network models using LSTM with different optimizers. The obtained results using LSTM with Nadam optimizer present a significant improvement in the forecasting accuracy of 30.56% over autoregressive integrated moving average, 47.48% over seasonal autoregressive integrated moving average, and 1.35%,

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A novel long term solar photovoltaic power forecasting approach using LSTM with Nadam optimizer: A case study of India

Sharma

Soni

Paliwal

et al. 2022

Energy Science & Engineering

View full text Add to dashboard Cite

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“…One of the reasons why its performance is not suitable is the nonlinearity of the problem to be modeled. Intelligent systems are used in some different applications with very satisfactory performance in general terms [22] [23] [24] [25] [26]. Of course the non-linearity problem could be solved in many cases with the use of soft-computing techniques [27] [28] [29] [30].…”

Section: Introductionmentioning

confidence: 99%

Hydrogen consumption prediction of a fuel cell based system with a hybrid intelligent approach

Sousa

Aláiz‐Moretón

Quintián

et al. 2020

Energy

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Energy storage is one of the challenges of the electric sector. There are several different technologies available for facing it, from the traditional ones to the most advanced. With the current trend, it is mandatory to develop new energy storage systems that allow optimal efficiency, something that does not happen with traditional ones. Another feature that new systems must meet is to envisage the behavior of energy generation and consumption. With this aim, the present research deals the hydrogen consumption prediction of a fuel cell based system thanks a hybrid intelligent approach implementation.

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“…In the state of the art, there are studies that address the topic of predicting the generation of solar energy [19,20]. In [21], the authors propose a hybrid model that combines machine-learning methods with a theta statistical method for a more accurate prediction of future solar power generation from renewable energy plants.…”

Section: Introductionmentioning

confidence: 99%

Solar Thermal Collector Output Temperature Prediction by Hybrid Intelligent Model for Smartgrid and Smartbuildings Applications and Optimization

et al. 2020

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Currently, there is great interest in reducing the consumption of fossil fuels (and other non-renewable energy sources) in order to preserve the environment; smart buildings are commonly proposed for this purpose as they are capable of producing their own energy and using it optimally. However, at times, solar energy is not able to supply the energy demand fully; it is mandatory to know the quantity of energy needed to optimize the system. This research focuses on the prediction of output temperature from a solar thermal collector. The aim is to measure solar thermal energy and optimize the energy system of a house (or building). The dataset used in this research has been taken from a real installation in a bio-climate house located on the Sotavento Experimental Wind Farm, in north-west Spain. A hybrid intelligent model has been developed by combining clustering and regression methods such as neural networks, polynomial regression, and support vector machines. The main findings show that, by dividing the dataset into small clusters on the basis of similarity in behavior, it is possible to create more accurate models. Moreover, combining different regression methods for each cluster provides better results than when a global model of the whole dataset is used. In temperature prediction, mean absolute error was lower than 4 ∘ C.

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A Hybrid Intelligent System to forecast solar energy production

Cited by 22 publications

References 21 publications

A novel long term solar photovoltaic power forecasting approach using LSTM with Nadam optimizer: A case study of India

A novel long term solar photovoltaic power forecasting approach using LSTM with Nadam optimizer: A case study of India

Hydrogen consumption prediction of a fuel cell based system with a hybrid intelligent approach

Solar Thermal Collector Output Temperature Prediction by Hybrid Intelligent Model for Smartgrid and Smartbuildings Applications and Optimization

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