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

Abstract

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.

Keywords

• Flower pollination
• Gaussian
• IoT
• Price
• Spot market