Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach

Khan, Waqas Ahmad; Walker, Shalika; Zeiler, W Wim

doi:10.1016/j.energy.2021.122812

Cited by 187 publications

(48 citation statements)

References 42 publications

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“…However, in most cases, it varies; for example, nighttime temperatures vary in summer and winter. More than 12 w/m 2 sky radiation was considered at the time [1,20].…”

Section: Data Processingmentioning

confidence: 99%

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Machine Learning Approach on Time Series for PV-Solar Energy

Sivakumar

Neeraja²,

Rani³

et al. 2022

Advances in Materials Science and Engineering

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Solar energy is the kind of alternative energy that a photovoltaic (PV) system may generate. The PV system depends on the environmental conditions present at the moment, such as the humidity level, surface temperature of the PV system, and wind speed. These are the most important characteristics to consider when calculating the amount of electricity generated by the PV system. Therefore, the calculation of power production is not a simple task. In the meanwhile, we are working on developing the machine learning algorithm that will be used to estimate electricity production using the fundamental characteristics that are now accessible. Now, we begin the process of machine learning by developing a time series model since the essential parameters will change over time. We get preliminary information from four distinct locations around the country. We arrived at this conclusion by using the ANN and regression modeling approaches for best to create the most significant overall performance across all cities.

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“…However, in most cases, it varies; for example, nighttime temperatures vary in summer and winter. More than 12 w/m 2 sky radiation was considered at the time [1,20].…”

Section: Data Processingmentioning

confidence: 99%

“…ey can lower the cost of producing power by making use of renewable energy sources [1,2]. In recent years, a number of nations have placed a signi cant emphasis on solar energy systems as sources of renewable energy [3].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approach on Time Series for PV-Solar Energy

Sivakumar

Neeraja²,

Rani³

et al. 2022

Advances in Materials Science and Engineering

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“…The authors also compared their model performance using datasets collected from Brazil and Spain. Likewise, Khan et al [39] combined Artificial Neural Network (ANN), LSTM, and eXtreme Gradient Boosting (XGBoost) in their ensemble model to improve the generalization of solar forecasting. Their method achieved more stable performance in several case studies than using ANN, LSTM, or bagging alone.…”

Section: Literature Reviewmentioning

confidence: 99%

SENERGY: A Novel Deep Learning-Based Auto-Selective Approach and Tool for Solar Energy Forecasting

Alkhayat¹,

Hasan²,

Mehmood³

2022

Preprint

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The sustainability of the planet and its inhabitants is in dire danger and is among the highest priorities on global agendas such as the Sustainable Development Goals (SDGs) of the United Nations (UN). Solar energy -- among other clean, renewable, and sustainable energies -- is seen as essential for environmental, social, and economic sustainability. Predicting solar energy accurately is critical to increasing reliability and stability, and reducing the risks and costs of the energy systems and markets. Researchers have come a long way in developing cutting-edge solar energy forecasting methods. However, these methods are far from optimal in terms of their accuracies, generalizability, benchmarking, and other requirements. Particularly, no single method performs well across all climates and weathers due to the large variations in meteorological data. This paper proposes SENERGY (an acronym for Sustainable Energy), a novel deep learning-based auto-selective approach and tool that, instead of generalising a specific model for all climates, predicts the best performing deep learning model for GHI forecasting in terms of forecasting error. The approach is based on carefully devised deep learning methods and feature sets through an extensive analysis of deep learning forecasting and classification methods using ten meteorological datasets from three continents. We analyse the tool in great detail through a range of metrics and methods for performance analysis, visualization, and comparison of solar forecasting methods. SENERGY outperforms existing methods in all performance metrics including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Forecast Skills (FS), Relative Forecasting Error, and the normalised versions of these metrics. The proposed auto-selective approach can be extended to other research problems such as wind energy forecasting and predict forecasting models based on different criteria (in addition to the minimum forecasting error used in this paper) such as the energy required or speed of model execution, different input features, different optimisations of the same models, or other user preferences.

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“…feature selection). Results showed that the prediction accuracy is improved to each sole base model (ANN and SVR); Khan et al 45 proposed an improved generally applicable stacked ensemble algorithm of ANN and Long Short-Term Memory (LSTM) as base models, whose predictions were aggregated using an Extreme Gradient Boosting (XGBoost) algorithm, to enhance the predictability of the solar PV production. Results showed that the proposed ensemble approach outperformed each base model (ANN and LSTM) and Bagging ensemble learning method on two different case studies.…”

Section: Introductionmentioning

confidence: 99%

An adaptive approach-based ensemble for 1 day-ahead production prediction of solar PV systems

Al‐Dahidi

Muhsen

Sari

et al. 2022

Advances in Mechanical Engineering

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The world is becoming more reliant on renewable energy sources to satisfy its growing energy demand. The primary disadvantage of such sources is their significant uncertainty in power production. As appropriate energy production planning and scheduling necessitate a solid and confident assessment of renewable power production, the necessity for developing reliable prediction models grows by the day. This paper proposes an adaptive approach-based ensemble for 1-day ahead production prediction of solar Photovoltaic (PV) systems. Different ensembles of Artificial Neural Networks (ANNs) prediction models are established, whose architectures (number of the ANNs that comprise the ensembles) and configurations (number of hidden nodes required by the ANNs models of the ensembles) change adaptively at each hour h, h∈ [1, 24] of a day, for accommodating the hour seasonality in the solar PV data and, thus, enhancing the 1 day-ahead predictions accuracy. The suggested approach is tested on a 264 kW solar PV system installed at Applied Science Private University, Jordan. Its prediction performance is evaluated, particularly for different weather conditions (seasons) experienced by the concerned PV system, using standard performance metrics. Results show the effectiveness of the suggested approach in predicting solar PV power production and its superiority compared to another prediction approach of the literature that uses single ANNs at each hour h of the day. Specifically, for 1-day ahead prediction, the obtained enhanced accuracy, on average, was around 8%–10% on the test “unseen” datasets.

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Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach

Cited by 187 publications

References 42 publications

Machine Learning Approach on Time Series for PV-Solar Energy

Machine Learning Approach on Time Series for PV-Solar Energy

SENERGY: A Novel Deep Learning-Based Auto-Selective Approach and Tool for Solar Energy Forecasting

An adaptive approach-based ensemble for 1 day-ahead production prediction of solar PV systems

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