Hourly Forecasting of Solar Photovoltaic Power in Pakistan Using Recurrent Neural Networks

Khan, Sohrab; Shaikh, Faheemullah; Siddiqui, Mokhi Maan; Hussain, Tanweer; Kumar, Laveet; Nahar, Afroza

doi:10.1155/2022/7015818

Cited by 6 publications

(5 citation statements)

References 29 publications

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“…Khan et al [28] suggested that the most effective forecasting model for PV power output is recurrent neural networks. The data were provided from Quaid-e-Azam Solar Park in Bahawalpur, Pakistan, a 100 MW solar power plant.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimizing Short-Term Photovoltaic Power Forecasting: A Novel Approach with Gaussian Process Regression and Bayesian Hyperparameter Tuning

Islam,

Ghosh,

Faruque

et al. 2024

Processes

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The inherent volatility of PV power introduces unpredictability to the power system, necessitating accurate forecasting of power generation. In this study, a machine learning (ML) model based on Gaussian process regression (GPR) for short-term PV power output forecasting is proposed. With its benefits in handling nonlinear relationships, estimating uncertainty, and generating probabilistic forecasts, GPR is an appropriate approach for addressing the problems caused by PV power generation’s irregularity. Additionally, Bayesian optimization to identify optimal hyper-parameter combinations for the ML model is utilized. The research leverages solar radiation intensity data collected at 60-min and 30-min intervals over periods of 1 year and 6 months, respectively. Comparative analysis reveals that the data set with 60-min intervals performs slightly better than the 30-min intervals data set. The proposed GPR model, coupled with Bayesian optimization, demonstrates superior performance compared to contemporary ML models and traditional neural network models. This superiority is evident in 98% and 90% improvements in root mean square errors compared to feed-forward neural network and artificial neural network models, respectively. This research contributes to advancing accurate and efficient forecasting methods for PV power output, thereby enhancing the reliability and stability of power systems.

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Section: Literature Reviewmentioning

confidence: 99%

Optimizing Short-Term Photovoltaic Power Forecasting: A Novel Approach with Gaussian Process Regression and Bayesian Hyperparameter Tuning

Islam,

Ghosh,

Faruque

et al. 2024

Processes

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“…For an efficient utilization of a solar power plants, a cost benefit and techno economic analysis is very important for determining the optimum conditions for efficient operation [14]. In fact, as suggested by Khan et al [15], the peculiarity of a photovoltaic system is that it requires a strong commitment of initial capital and low maintenance costs (annually about 1% of the cost of the system) [16].…”

Section: Literature Reviewmentioning

confidence: 99%

The Cost Benefit Analysis of Commercial 100 MW Solar PV: The Plant Quaid-e-Azam Solar Power Pvt Ltd.

et al. 2022

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The energy crisis in Pakistan has crippled the country’s economy with an energy shortfall reaching up to 6000 MW. Fortunately, Pakistan lies close to the Sun Belt and therefore receives very high irradiation. To this end, in the beginning of 2014 the Pakistani government sanctioned a solar photovoltaic project namely Quaid-e-Azam Solar Park which was rated at 1000 MW. In this study, a cost benefit analysis for the Quaid-e-Azam Solar Park has been developed. The model uses RETScreen software. In fact, a literature review pointed out that most of the previous research work with reference to cost benefit analysis for solar projects has been mainly carried out on smaller power plants. The outcome of the study shows promising results with the simple payback period coming out at 5.6 years. Furthermore, this analysis can serve as guideline for future solar photovoltaic projects in Pakistan and can help in the development and utilization of the huge solar potential of the country, thus aiding in the reduction of energy shortage. In its proposal, our research is unique and innovative in the Pakistani context. The results aim to serve as a guideline for decision makers and researchers interested in this topic.

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“…It can distinguish impossible representations without any predetermined equations. Besides the previous methods, Recurrent Neural Network (RNN), 33 Feed-Forward Neural Network (FFNN), 34 and Feed-Back Neural Network (FBNN) have been deployed to predict the PV generation at various time horizons. 35 For example, Kumar et al 36 developed three real-time prediction models, namely the Elman Neural Network, FFNN, and Generalized Regression Neural Network (GRNN), for the short-term power production prediction of a Semi-Transparent PV (STPV) system.…”

Section: Literature Reviewmentioning

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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Hourly Forecasting of Solar Photovoltaic Power in Pakistan Using Recurrent Neural Networks

Cited by 6 publications

References 29 publications

Optimizing Short-Term Photovoltaic Power Forecasting: A Novel Approach with Gaussian Process Regression and Bayesian Hyperparameter Tuning

Optimizing Short-Term Photovoltaic Power Forecasting: A Novel Approach with Gaussian Process Regression and Bayesian Hyperparameter Tuning

The Cost Benefit Analysis of Commercial 100 MW Solar PV: The Plant Quaid-e-Azam Solar Power Pvt Ltd.

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

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