A Predictive Model for Solar Photovoltaic Power using the Levenberg-Marquardt and Bayesian Regularization Algorithms and Real-Time Weather Data

Alomari, Mohammad H.; Younis, Ola; Hayajneh, Sofyan

doi:10.14569/ijacsa.2018.090148

Cited by 7 publications

(6 citation statements)

References 22 publications

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“…Specifically, the former leads to more accurate predictions with RMSE equals to 19.24 kW, MAE equals to 10.84 kW, and R 2 equals to 91.59% compared to the values of the latter with RMSE equals to 23.68 kW, MAE equals to 16.44 kW, and R 2 equals to 85.28%, when the BR learning algorithm being used. In addition, the BR shows the best in Alomari et al (2018b) when looking at the predictions accuracy without any consideration of the computational efforts required for building/developing/optimizing the ANN prediction models, whereas in this work, the computational efforts are considered and showed that a compromise between the predictions accuracy FIGURE 7 | Average performance metrics obtained by the ANN models on the test dataset over the 5-CV using the D t 8 set of features compared to the two P Benchmarks. and the computational efforts has to be taken into account.…”

Section: Resultsmentioning

confidence: 86%

“…Results showed that the ANN characterized by one hidden layer, LinearSigmoidAxon as a neuron activation function, and Conjugate Gradient as a learning algorithm is capable of providing accurate solar power predictions. Alomari et al (2018b) proposed a prediction model for solar PV power production based on ANN. The proposed model explored the capabilities of two learning algorithms, namely Levenberg-Marquardt (LM) and Bayesian Regularizations (BR), using different combinations of the time stamp and the realtime weather features (i.e., ambient temperature and global solar radiation).…”

Section: Introductionmentioning

confidence: 99%

“…In this context, the objective of this work evolves from that in Alomari et al (2018b) by developing an ANN in which different learning algorithms and different training datasets (i.e., sets of real-time weather and PV power production data) are investigated for accurate 1 day-ahead power prediction with short computational time. Specifically, 10 different learning algorithms are investigated in terms of their capability of optimally setting the internal parameters of the ANN model, and 23 different combinations of the time stamp of the year and the historical wind speed, ambient temperature, global solar radiation, while incorporating the historical PV power productions, are examined for developing an efficient solar PV power prediction model.…”

Section: Introductionmentioning

confidence: 99%

“…Wind speed (at different levels of 10, 35, and 36 m) (m/s); • Wind direction (at different levels of 10, 35, and 36 m); • Ambient temperature (at different levels of 1, and 35 m) ( • C); • Relative humidity (at different levels of 1 and 35 m) (%); • Barometric pressure (hPa); • Precipitation amounts (mm); • Global and diffuse solar irradiances (W/m 2 ); • Soil surface temperature ( • C); • Subsoil temperature ( • C).The influence of the weather features on the predictability of the PV power productions has been investigated inAlomari et al (2018b) by developing different ANN models trained with different combinations of time and weather inputs. A combination of the current time stamp in hours, at time t, t ∈ […”

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confidence: 99%

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Assessment of Artificial Neural Networks Learning Algorithms and Training Datasets for Solar Photovoltaic Power Production Prediction

et al. 2019

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The capability of accurately predicting the Solar Photovoltaic (PV) power productions is crucial to effectively control and manage the electrical grid. In this regard, the objective of this work is to propose an efficient Artificial Neural Network (ANN) model in which 10 different learning algorithms (i.e., different in the way in which the adjustment on the ANN internal parameters is formulated to effectively map the inputs to the outputs) and 23 different training datasets (i.e., different combinations of the real-time weather variables and the PV power production data) are investigated for accurate 1 day-ahead power production predictions with short computational time. In particular, the correlations between different combinations of the historical wind speed, ambient temperature, global solar radiation, PV power productions, and the time stamp of the year are examined for developing an efficient solar PV power production prediction model. The investigation is carried out on a 231 kW ac grid-connected solar PV system located in Jordan. An ANN that receives in input the whole historical weather variables and PV power productions, and the time stamp of the year accompanied with Levenberg-Marquardt (LM) learning algorithm is found to provide the most accurate predictions with less computational efforts. Specifically, an enhancement reaches up to 15, 1, and 5% for the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R 2) performance metrics, respectively, compared to the Persistence prediction model of literature.

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Section: Resultsmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Assessment of Artificial Neural Networks Learning Algorithms and Training Datasets for Solar Photovoltaic Power Production Prediction

et al. 2019

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“…In single layer architecture, we used the tansig transfer function in the hidden layer and the purelin transfer function in the output layer. Alomari et al (2018) and Quintana et al (2011) say that this network can ballpark any function with a limited number of discontinuities if the appropriate number of neurons are provided.…”

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confidence: 99%

Security as a Solution: An Intrusion Detection System Using a Neural Network for IoT Enabled Healthcare Ecosystem

Jain¹,

Singh²,

Sharma

2021

IJIKM

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Aim/Purpose: The primary purpose of this study is to provide a cost-effective and artificial intelligence enabled security solution for IoT enabled healthcare ecosystem. It helps to implement, improve, and add new attributes to healthcare services. The paper aims to develop a method based on an artificial neural network technique to predict suspicious devices based on bandwidth usage. Background: COVID has made it mandatory to make medical services available online to every remote place. However, services in the healthcare ecosystem require fast, uninterrupted facilities while securing the data flowing through them. The solution in this paper addresses both the security and uninterrupted services issue. This paper proposes a neural network based solution to detect and disable suspicious devices without interrupting critical and life-saving services. Methodology: This paper is an advancement on our previous research, where we performed manual knowledge-based intrusion detection. In this research, all the experiments were executed in the healthcare domain. The mobility pattern of the devices was divided into six parts, and each one is assigned a dedicated slice. The security module regularly monitored all the clients connected to slices, and machine learning was used to detect and disable the problematic or suspicious devices. We have used MATLAB’s neural network to train the dataset and automatically detect and disable suspicious devices. The different network architectures and different training algorithms (Levenberg–Marquardt and Bayesian Framework) in MATLAB software have attempted to achieve more precise values with different properties. Five iterations of training were executed and compared to get the best result of R=99971. We configured the application to handle the four most applicable use cases. We also performed an experimental application simulation for the assessment and validation of predictions. Contribution: This paper provides a security solution for the IoT enabled healthcare system. The architectures discussed suggest an end-to-end solution on the sliced network. Efficient use of artificial neural networks detects and block suspicious devices. Moreover, the solution can be modified, configured and deployed in many other ecosystems like home automation. Findings: This simulation is a subset of the more extensive simulation previously performed on the sliced network to enhance its security. This paper trained the data using a neural network to make the application intelligent and robust. This enhancement helps detect suspicious devices and isolate them before any harm is caused on the network. The solution works both for an intrusion detection and prevention system by detecting and blocking them from using network resources. The result concludes that using multiple hidden layers and a non-linear transfer function, logsig improved the learning and results. Recommendations for Practitioners: Everything from offices, schools, colleges, and e-consultation is currently happening remotely. It has caused extensive pressure on the network where the data flowing through it has increased multifold. Therefore, it becomes our joint responsibility to provide a cost-effective and sustainable security solution for IoT enabled healthcare services. Practitioners can efficiently use this affordable solution compared to the expensive security options available in the commercial market and deploy it over a sliced network. The solution can be implemented by NGOs and federal governments to provide secure and affordable healthcare monitoring services to patients in remote locations. Recommendation for Researchers: Research can take this solution to the next level by integrating artificial intelligence into all the modules. They can augment this solution by making it compatible with the federal government’s data privacy laws. Authentication and encryption modules can be integrated to enhance it further. Impact on Society: COVID has given massive exposure to the healthcare sector since last year. With everything online, data security and privacy is the next most significant concern. This research can be of great support to those working for the security of health care services. This paper provides “Security as a Solution”, which can enhance the security of an otherwise less secure ecosystem. The healthcare use cases discussed in this paper address the most common security issues in the IoT enabled healthcare ecosystem. Future Research: We can enhance this application by including data privacy modules like authentication and authorisation, data encryption and help to abide by the federal privacy laws. In addition, machine learning and artificial intelligence can be extended to other modules of this application. Moreover, this experiment can be easily applicable to many other domains like e-homes, e-offices and many others. For example, e-homes can have devices like kitchen equipment, rooms, dining, cars, bicycles, and smartwatches. Therefore, one can use this application to monitor these devices and detect any suspicious activity.

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Application of machine learning methods in photovoltaic output power prediction: A review

Zhang

2022

Journal of Renewable and Sustainable Energy

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As the proportion of photovoltaic (PV) power generation rapidly increases, accurate PV output power prediction becomes more crucial to energy efficiency and renewable energy production. There are numerous approaches for PV output power prediction. Many researchers have previously summarized PV output power prediction from different angles. However, there are relatively few studies that use machine learning methods as a means to conduct a separate review of PV output power prediction. This review classifies machine learning methods from different perspectives and provides a systematic and critical review of machine learning methods for recent PV output power applications in terms of the temporal and spatial scales of prediction and finds that the artificial neural network and support vector machine are used much more frequently than other methods. In addition, this study examines the differences between the output power prediction of individual PV plants and regional PV stations and the benefits of regional PV plant prediction, while this paper presents some performance evaluation matrices commonly used for PV output power prediction. In addition, to further improve the accuracy of machine learning methods for PV output power prediction, some researchers suggest preprocessing the input data of the prediction models or considering hybrid machine learning methods. Furthermore, the potential advantages of machine model optimization for prediction performance improvement are discussed and explored in detail.

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A Predictive Model for Solar Photovoltaic Power using the Levenberg-Marquardt and Bayesian Regularization Algorithms and Real-Time Weather Data

Cited by 7 publications

References 22 publications

Assessment of Artificial Neural Networks Learning Algorithms and Training Datasets for Solar Photovoltaic Power Production Prediction

Assessment of Artificial Neural Networks Learning Algorithms and Training Datasets for Solar Photovoltaic Power Production Prediction

Security as a Solution: An Intrusion Detection System Using a Neural Network for IoT Enabled Healthcare Ecosystem

Application of machine learning methods in photovoltaic output power prediction: A review

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