A deep learning-based ensemble stacking (DSE-XGB) approach is proposed for Solar PV energy generation forecast. A detailed comparison between individual deep learning models, bagging and the proposed model is presented. The models are evaluated on two case studies (5 dataset) from different locations with 15-min and 1-h data resolution.
However, few studies have used stacking models to predict photovoltaic power generation. In the research, we develop four different stacking models that are based on extreme gradient boosting, random forest, light gradient boosting, and gradient boosting decision tree to predict photovoltaic power generation, by using two datasets.
The predictions from the base models are integrated using an extreme gradient boosting algorithm to enhance the accuracy of the solar PV generation forecast. The proposed model was evaluated on four different solar generation datasets to provide a comprehensive assessment.
The proposed model had a variance of about 4%–5% and was holding consistently even with the change in the data at the base level. The non-reliance of deep ensemble stacking only on the input data makes it more reliable for use in solar PV generation forecast. Table 7.
Despite the clean and renewable advantages of solar energy, the instability of photovoltaic power generation limits its wide applicability. In order to ensure stable power-grid operations and the safe dispatching of the power grid, it is necessary to develop a model that can accurately predict the photovoltaic power generation.
The accurate forecasting of PV power generation is helpful for grid-planning improvement, scheduling optimization, and management development. Machine-learning models have high predictive accuracies, and they are widely used in various fields, including in photovoltaic power generation.
Most of the previous research in deep ensemble learning has treated Solar PV generation only as a regression task [, , , , , , , , , , , , , , , , , , , , , ] by only using artificial neural network models and statistical models at the base level.
A deep learning-based ensemble stacking (DSE-XGB) approach is proposed for Solar PV energy generation forecast. A detailed comparison between individual deep learning models, bagging and the proposed model is presented. The models are evaluated on two case studies (5 dataset) from different locations with 15-min and 1-h data resolution.
Accurate forecasting of wind and PV power generation enables timely scheduling and control of exchange power, preventing off-grid events caused by increased …
In this paper, ensemble-based machine learning models with gradient boosting machine and random forest are proposed for predicting the power production from six different solar PV systems. The models are based on three year''s performance of a 1.2 MW grid-integrated solar photo-voltaic (PV) power plant.
We first summarized individual and hybrid deep learning models for electrical demand prediction and solar photovoltaic power generation forecasting. In addition, we highlighted the most relevant recent works for …
In this work, statistical methods based on multiregression analysis and the Elmann artificial neural network (ANN) have been developed in order to predict power production of a 960 kW P grid-connected PV plant …
The new annual power generation estimation method based on radiation frequency distribution (RSD method) proposed in this paper mainly combines outdoor solar radiation and indoor artificial light systems to estimate the annual power generation of solar photovoltaic systems.
Time series forecasting of solar power generation for large-scale photovoltaic plants Renew Energy, 150 ( 2020 ), pp. 797 - 807, 10.1016/j.renene.2019.12.131 View PDF View article View in Scopus Google Scholar
In this paper, ensemble-based machine learning models with gradient boosting machine and random forest are proposed for predicting the power production from six different solar PV …
Solar photovoltaic (PV) power generation is the process of converting energy from the sun into electricity using solar panels. Solar panels, also called PV panels, are combined into arrays in a PV system. PV systems can also be installed in grid-connected or off-grid (stand-alone) configurations. The basic components of these two configurations ...
We first summarized individual and hybrid deep learning models for electrical demand prediction and solar photovoltaic power generation forecasting. In addition, we highlighted the most relevant recent works for power forecasting with the highest accuracy.
In the research, we develop four different stacking models that are based on extreme gradient boosting, random forest, light gradient boosting, and gradient boosting decision tree to predict photovoltaic power generation, by using two datasets.
Solar photovoltaic (PV) power generation is susceptible to environmental factors, and redundant features can disrupt prediction accuracy. To achieve rapid and accurate online prediction, we ...
This is due to the high cost of the modules, inverter, cables, and other components in the SPV power generation system. The need for optimal design has inspired …
The coefficient of determination, R2, is used to measure the proportion of variation in photovoltaic power generation that can be explained by the model''s variables, while gCO2eq represents the ...
This is due to the high cost of the modules, inverter, cables, and other components in the SPV power generation system. The need for optimal design has inspired many authors. For instance, using the Transient Energy System Simulation Program software (TRNSYS), built a deterministic sizing technique for a household HESS. The Strength Pareto …
The new annual power generation estimation method based on radiation frequency distribution (RSD method) proposed in this paper mainly combines outdoor solar …
In the research, we develop four different stacking models that are based on extreme gradient boosting, random forest, light gradient boosting, and gradient boosting …
In this work, statistical methods based on multiregression analysis and the Elmann artificial neural network (ANN) have been developed in order to predict power production of a 960 kW P grid-connected PV plant installed in Italy.
Accurate forecasting of wind and PV power generation enables timely scheduling and control of exchange power, preventing off-grid events caused by increased penetration of wind and solar energy, thus ensuring stable power system operation.
In the research, we develop four different stacking models that are based on extreme gradient boosting, random forest, light gradient boosting, and gradient boosting decision tree to predict...
Hence, accurate solar Photovoltaic (PV) power forecasting is essential to maintain system reliability and maximize renewable energy integration. The current solar PV power forecasting approaches ...
Sustainability 2022, 14, 5669 3 of 16 the prediction of photovoltaic power generation. (3) Previous studies mainly used a single data source; however, this study used Australian panel and Chinese ...
In this regard, this paper proposes a stacked ensemble algorithm (Stack-ETR) to forecast PV output power one day ahead, utilizing three machine learning (ML) algorithms, namely, random forest regressor (RFR), extreme …
A deep learning-based ensemble stacking (DSE-XGB) approach is proposed for Solar PV energy generation forecast. A detailed comparison between individual deep learning models, bagging and the proposed model is presented. The models are evaluated on two case …
Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach Waqas Khan*, Shalika Walker, Wim Zeiler Eindhoven University of Technology, Netherlands article info Article history: Received 16 July 2021 Received in revised form 30 November 2021 Accepted 2 December 2021 Available online 4 December 2021 …
Nowadays, photovoltaics (PV) has gained popularity among other renewable energy sources because of its excellent features. However, the instability of the system''s output has become a critical problem due to the high …
Observing Fig. 7, Fig. 8, Fig. 9, Fig. 10, the operational solar power generated for the implicit empirical model is far from reaching the design capacity in Table 1, and the operational solar generation data for the explicit (double), minimize the gap between the design and operational solar power capacities and operational solar power generation data for explicit …
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