Novel Gaussian flower pollination algorithm with IoT for unit price prediction in peer-to-peer energy trading market

In order to enhance the operational cost and efficient management of all the power system equipment in a micro grid requires proper forecasting of energy and scheduled power dispatch. Due to the uncertainties in the load demand and the interconnection of intermittent source of energy, the operationa...

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Autores principales:	Satyabrata Sahoo, Saratchandra Swain, Ritesh Dash, Sanjeevikumar P., Jyotheeswara Reddy K., Vivekanandan Subburaj
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Flower pollination Gaussian IoT Price Spot market Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/cacf654669e14d7a9907f5a8bf0730d3
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spelling	oai:doaj.org-article:cacf654669e14d7a9907f5a8bf0730d32021-11-28T04:34:09ZNovel Gaussian flower pollination algorithm with IoT for unit price prediction in peer-to-peer energy trading market2352-484710.1016/j.egyr.2021.08.170https://doaj.org/article/cacf654669e14d7a9907f5a8bf0730d32021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352484721007733https://doaj.org/toc/2352-4847In order to enhance the operational cost and efficient management of all the power system equipment in a micro grid requires proper forecasting of energy and scheduled power dispatch. Due to the uncertainties in the load demand and the interconnection of intermittent source of energy, the operational cost becomes high. The traditional grid also requires accurate prediction of per unit price in the electricity trading market. Electricity price forecasting plays a vital role. Time series based machine learning algorithm are generally used to calculate unit price in lieu of power loss in a smart grid architecture. However, while dealing with large data set, generated in every 15 s, it is very challenging and time consuming and at the same time large data set may create curve over fittings. In this paper, a novel approach has been made by combining both flower pollination algorithm and machine learning for forecasting the unit price. The proposed model comprises of three basic models such as feature selection, principal component analysis and novel hybrid model for optimization and regression. Three different sigma value such as 0.8,0.9 & 0.94 with Gaussian surface has been used to test the algorithm Finally, the algorithm has been tested with IoT architecture for robustness evaluation.Satyabrata SahooSaratchandra SwainRitesh DashSanjeevikumar P.Jyotheeswara Reddy K.Vivekanandan SubburajElsevierarticleFlower pollinationGaussianIoTPriceSpot marketElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENEnergy Reports, Vol 7, Iss , Pp 8265-8276 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Flower pollination Gaussian IoT Price Spot market Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Flower pollination Gaussian IoT Price Spot market Electrical engineering. Electronics. Nuclear engineering TK1-9971 Satyabrata Sahoo Saratchandra Swain Ritesh Dash Sanjeevikumar P. Jyotheeswara Reddy K. Vivekanandan Subburaj Novel Gaussian flower pollination algorithm with IoT for unit price prediction in peer-to-peer energy trading market
description	In order to enhance the operational cost and efficient management of all the power system equipment in a micro grid requires proper forecasting of energy and scheduled power dispatch. Due to the uncertainties in the load demand and the interconnection of intermittent source of energy, the operational cost becomes high. The traditional grid also requires accurate prediction of per unit price in the electricity trading market. Electricity price forecasting plays a vital role. Time series based machine learning algorithm are generally used to calculate unit price in lieu of power loss in a smart grid architecture. However, while dealing with large data set, generated in every 15 s, it is very challenging and time consuming and at the same time large data set may create curve over fittings. In this paper, a novel approach has been made by combining both flower pollination algorithm and machine learning for forecasting the unit price. The proposed model comprises of three basic models such as feature selection, principal component analysis and novel hybrid model for optimization and regression. Three different sigma value such as 0.8,0.9 & 0.94 with Gaussian surface has been used to test the algorithm Finally, the algorithm has been tested with IoT architecture for robustness evaluation.
format	article
author	Satyabrata Sahoo Saratchandra Swain Ritesh Dash Sanjeevikumar P. Jyotheeswara Reddy K. Vivekanandan Subburaj
author_facet	Satyabrata Sahoo Saratchandra Swain Ritesh Dash Sanjeevikumar P. Jyotheeswara Reddy K. Vivekanandan Subburaj
author_sort	Satyabrata Sahoo
title	Novel Gaussian flower pollination algorithm with IoT for unit price prediction in peer-to-peer energy trading market
title_short	Novel Gaussian flower pollination algorithm with IoT for unit price prediction in peer-to-peer energy trading market
title_full	Novel Gaussian flower pollination algorithm with IoT for unit price prediction in peer-to-peer energy trading market
title_fullStr	Novel Gaussian flower pollination algorithm with IoT for unit price prediction in peer-to-peer energy trading market
title_full_unstemmed	Novel Gaussian flower pollination algorithm with IoT for unit price prediction in peer-to-peer energy trading market
title_sort	novel gaussian flower pollination algorithm with iot for unit price prediction in peer-to-peer energy trading market
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/cacf654669e14d7a9907f5a8bf0730d3
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Novel Gaussian flower pollination algorithm with IoT for unit price prediction in peer-to-peer energy trading market

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