Quantum Neural Networks (QNN) Application in Weather Prediction of Smart Grids

Safari, Ashkan; Ghavifekr, Amir Aminzadeh

doi:10.1109/sgc54087.2021.9664117

Cited by 21 publications

(4 citation statements)

References 11 publications

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“…As the utilization of DeepOptaCap presented in NOS of capacitors banks, besides improving the application of artificial intelligence in this field, other several intelligent models can be applied to further considerations. Particularly, quantum technology [52] is one of these critical fields that investments are being devoted to its advanced and complex potentials [53]. One of the alternatives is quantum neural networks (QNNs) [54] that can be applied to forecasting, optimal planning, and classification fields of smart power systems, and capacitor banks‐incorporated power distribution systems are not an exception.…”

Section: Final Step: Deepoptacapmentioning

confidence: 99%

Net saving improvement of capacitor banks in power distribution systems by increasing daily size switching number: A comparative result analysis by artificial intelligence

Sadeghian,

Safari

2024

The Journal of Engineering

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This paper studies the effect of the number of switching (NOS) per day of capacitor banks on loss reduction in radial distribution systems. To this aim, the daytime (more precisely, 24 h) is divided into different numbers of time segments (equal to the same NOS) for capacitors’ size switching. The resulting non‐linear programming with discontinuous derivatives (called DNLP) model is solved subject to related constraints. The results reveal the impact of hourly switching of capacitor banks on further loss reduction (namely 118.4435, 83.7856, and 101.738 MWh for three IEEE systems) and higher net savings (i.e. k$5.6067, k$4.2772, and k$5.3542 for the same systems) of radial distribution systems compared to daily switching. Then, the hyper‐tuned Random Forest model is trained based on the IEEE 69‐bus network, fine‐tuned by the IEEE 10‐bus network, and fitted by the IEEE 33‐bus network to have an intelligent multi‐classification task with the highest accuracy. Numerical simulation, in both classic and intelligent parts, is presented to demonstrate the performance of DeepOptaCap. For the final step, DeepOptaCast is compared to other intelligent models of Light Gradient Boosting Method (LGBM), Decision Tree, and XGBoost, regarding KPIs of mean absolute percentage error, root mean squared percentage error, mean absolute error, root mean squared error, and coefficient of determination to demonstrate the model's superiority.

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Section: Final Step: Deepoptacapmentioning

confidence: 99%

Net saving improvement of capacitor banks in power distribution systems by increasing daily size switching number: A comparative result analysis by artificial intelligence

Sadeghian,

Safari

2024

The Journal of Engineering

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“…Now a days unpredictable rainfall results in flooding, affect on various human activities,sewage networks,public infrastructure and crop cultivation.So in this paper they comapre ANN and Deep Learning method in terms of their efficiency.So it showing ANN is outperformed than Deep Learning. Ashkan Safari,Amir Aminzadeh Ghavifekr [4] explain about Quantum Neural Network used in weather prediction in smart grids.Quantum technology, how it can be used to predict the weather for smart grids.It analyze what's happening in this field right now and in the future.Smart grids are really important for smart cities,factors like Demand response and weather forecasting are crucial elements that play a big part.So for weather prediction it uses highly accurate model to predict weather,so they combined AI and NN which is more efficient.But smart grid which having large amount of data,so AI and ANN approach is inefficient.So in this paper they combined Quantum technology and QNN which having the potential to predict weather with higher accuracy and execution speed.So in this paper they comare QNN and ANN in terms of accuracy for large amount of data. Kalpana Murugan,Ravi Kiran Tiruveedhi,Dinesh Reddy Ramireddygari,Deepika Thota,Chandralekha Neeli author shows [5] comparitive study for weather monitoring system.Multiple existing systems are available for weather monitoring and control but they are facing cost challenges due to their broad city wide coverage rather than focusing on specific locations .…”

Section: Literature Surveymentioning

confidence: 99%

Weather Forecasting: A Comprehensive Exploration of IoT and Machine Learning

2024

IJSREM

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Weather forecasting play an important role in human lives.It is impacting on various human activities such as agriculture and transportation.Changes in climate conditions often lead to unpredictable weather. Conventional forecasting models sometimes provide inaccurate predictions because they don't adapt well to the dynamic nature of climate.The integration of (IoT) and Machine Learning (ML) technologies has revolutionized weather forecasting methodologies.This paper explores how combining Machine Learning (ML) algorithms with Internet of Things (IoT) devices improves weather forecasting accuracy and efficiency. By analyzing real-time data and historical patterns, this paper shows how IoT and ML can offer more accurate and timely forecasts compare to the limitations of traditional methods.Accurate weather forecasts are really important for things like farming, transportation, saving energy.In this paper the effectiveness of the algorithm is evaluated using various performance metrics including r2 score comparison,mean square error comparison. Key Words: IOT,Machine Learning,Support Vector Machine

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“…Furthermore, reference [35] discusses the significance of AI in smart grids. Reference [36] presents quantum technologies in the context of smart grids, with a focus on predictive characteristics. Reference [37] uses precise AI-driven estimators on a dataset.…”

Section: Introductionmentioning

confidence: 99%

DeepVELOX: INVELOX Wind Turbine Intelligent Power Forecasting Using Hybrid GWO–GBR Algorithm

Safari,

Kheirandish Gharehbagh,

Nazari Heris

2023

Energies

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The transition to sustainable electricity generation depends heavily on renewable energy sources, particularly wind power. Making precise forecasts, which calls for clever predictive controllers, is a crucial aspect of maximizing the efficiency of wind turbines. This study presents DeepVELOX, a new methodology. With this method, sophisticated machine learning methods are smoothly incorporated into wind power systems. The Increased Velocity (IN-VELOX) wind turbine framework combines the Gradient Boosting Regressor (GBR) with the Grey Wolf Optimization (GWO) algorithm. Predictive capabilities are entering a new age thanks to this integration. This research presents DeepVELOX, its structure, and results. In particular, this study presents the considerable performance of DeepVELOX. With a MAPE of 0.0002 and an RMSPE of 0.0974, it gets outstanding Key Performance Indicator (KPI) results. The criteria of Accuracy, F1-Score, R2-Score, Precision, and Recall, with a value of 1, further emphasize its performance. The result of this process is an MSE of 0.0352. The significant reduction in forecast disparities is made possible by this system’s remarkable accuracy. Along with improving accuracy, the integration of machine learning algorithms, including GBR, the GWO algorithm, and wind turbine operations, offer a dynamic framework for maximizing power and energy capture.

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Quantum Neural Networks (QNN) Application in Weather Prediction of Smart Grids

Cited by 21 publications

References 11 publications

Net saving improvement of capacitor banks in power distribution systems by increasing daily size switching number: A comparative result analysis by artificial intelligence

Net saving improvement of capacitor banks in power distribution systems by increasing daily size switching number: A comparative result analysis by artificial intelligence

Weather Forecasting: A Comprehensive Exploration of IoT and Machine Learning

DeepVELOX: INVELOX Wind Turbine Intelligent Power Forecasting Using Hybrid GWO–GBR Algorithm

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