A Practical Approach for Predicting Power in a Small-Scale Off-Grid Photovoltaic System using Machine Learning Algorithms

Patel, Aadyasha; Swathika, O.V. Gnana; Subramaniam, Umashankar; Babu, Thanikanti Sudhakar; Tripathi, Alok; Nag, Samriddha; Karthick, Alagar; Muhibbullah, M.

doi:10.1155/2022/9194537

Cited by 30 publications

(17 citation statements)

References 80 publications

(53 reference statements)

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“…The extraction of features is an important step in predicting human age. Active appearance model (AAM) [35], local binary patterns (LBP) [36][37][38], anthropometric features [39], and biologically inspired features (BIF) [40] are some of the feature extraction approaches that have been developed.…”

Section: Face Detection and Identificationmentioning

confidence: 99%

Prediction of the Age and Gender Based on Human Face Images Based on Deep Learning Algorithm

Haseena¹,

Subbaraj²,

Madavan³

et al. 2022

Computational and Mathematical Methods in Medicine

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In recent times, nutrition recommendation system has gained increasing attention due to their need for healthy living. Current studies on the food domain deal with a recommendation system that focuses on independent users and their health problems but lack nutritional advice to individual users. The proposed system is developed to suggest nutritional food to people based on age and gender predicted from their face image. The designed methodology preprocesses the input image before performing feature extraction using the deep convolution neural network (DCNN) strategy. This network extracts D -dimensional characteristics from the source face image, followed by the feature selection strategy. The face’s distinctive and identifiable traits are chosen utilizing a hybrid particle swarm optimization (HPSO) technique. Support vector machine (SVM) is used to classify a person’s age and gender. The nutrition recommendation system relies on the age and gender classes. The proposed system is evaluated using classification rate, precision, and recall using Adience dataset and UTKface dataset, and real-world images exhibit excellent performance by achieving good prediction results and computation time.

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Section: Face Detection and Identificationmentioning

confidence: 99%

Prediction of the Age and Gender Based on Human Face Images Based on Deep Learning Algorithm

Haseena¹,

Subbaraj²,

Madavan³

et al. 2022

Computational and Mathematical Methods in Medicine

Self Cite

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“…For social hunting, the whole community is very well coordinated. The prey encircling process may be mathematically stated as follows [23][24][25]:…”

Section: Grey Wolf Optimizer (Gwo) Algorithmmentioning

confidence: 99%

Performance Prediction of Building Integrated Photovoltaic System Using Hybrid Deep Learning Algorithm

Ragupathi

Rengaraj²

2022

International Journal of Photoenergy

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In a grid-connected photovoltaic system, forecasting is a necessary and critical step. Solar Power is very nonlinear; this article develops and analyses building integrated photovoltaic (BIPV) forecasting algorithms for different timeframes, such as an hour, a day, and a week ahead, to manage grid operation effectively. However, a model built for a certain time scale may improve performance at that time scale but cannot be utilized to make predictions at other time scales. Here, we demonstrate how to use the multitask learning algorithm to create a multitime scale model for solar BIPV forecasting. Effective resource distribution across several tasks is shown. The suggested multitask learning approach is implemented using LSTM neural networks and evaluated over a range of horizons. We employed a modified version of the Chicken Swarm Optimizer (CSO) that takes the best features of the CSO and the GWO algorithms and merges them into one efficient approach to estimate the hyperparameters of the proposed LSTM model. The proposed approach consistently outperformed state-of-the-art single-timescale forecasting algorithms across all time scales.

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“…Gambar 1. Bentuk fisik NodeMcu b. Sensor ACS712 30A Sensor arus ACS712 bekerja dengan menggunakan prinsip hall effect [12], sensor ini berfungsi untuk membaca arus yang mengalir pada kabel penghubung beban listrik. Hall effect bekerja berdasarkan medan magnet yang disebabkan oleh arus listrik yang mengalir di dekat sensor [13].…”

Section: A Nodemcuunclassified

Purwarupa Smart Home menggunakan MQTT Broker untuk Memonitor Kondisi Abnormal Perangkat Listrik

Liandana

Nirmala²,

Hendayanti³

et al. 2022

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Smart home telah berkembang pesat, salah satu fitur yang banyak dimiliki oleh sistem smart home adalah untuk melakukan pengontrolan dan monitoring perangkat listrik yang ada di rumah. Pengontrolan bertujuan untuk menyalakan atau mematikan perngkat listrik, sedangkan monitoring bertujuan untuk mengetahui kondisi dari perangkat listrik apakah sudah menyala atau belum. Selain untuk memonitoring perangkat listrik, fokus dari penelitian ini adalah untuk memonitoring perangkat listrik yang gagal menyala karena mengalami kerusakan atau perangkat terputus dengan sumber tegangannya, dalam penelitian ini disebut dengn istilah abnormal atau anomali. Purwarupa dari smart home yang telah dikembangkan menggunakan protokol MQTT, sensor arus ACS712, NodeMcu, dan aplikasi web. Purwarupa smarthome telah dapat melakukan monitoring dan pengontrolan perangkat listrik dan memberikan informasi jika perangkat gagal menyala. Waktu yang diperlukan untuk menyalakan atau mematikan perangkat listrik berkisar dari 1.4 detik hingga 1.5 detik.

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A Practical Approach for Predicting Power in a Small-Scale Off-Grid Photovoltaic System using Machine Learning Algorithms

Cited by 30 publications

References 80 publications

Prediction of the Age and Gender Based on Human Face Images Based on Deep Learning Algorithm

Prediction of the Age and Gender Based on Human Face Images Based on Deep Learning Algorithm

Performance Prediction of Building Integrated Photovoltaic System Using Hybrid Deep Learning Algorithm

Purwarupa Smart Home menggunakan MQTT Broker untuk Memonitor Kondisi Abnormal Perangkat Listrik

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