Advanced Ensemble Model for Solar Radiation Forecasting Using Sine Cosine Algorithm and Newton’s Laws

El-Sayed M El-Kenawy,Zeeshan Shafi Khan,Sherif S M Ghoneim,Seyedali Mirjalili,Marwa Metwally Eid,Abdelhameed Ibrahim,M El-Said

doi:10.1109/access.2021.3106233

Abstract

As research in alternate energy sources is growing, solar radiation is catching the eyes of the research community immensely. Since solar energy generation depends on uncontrollable natural variables, without proper forecasting, this energy source cannot be trusted. For this forecasting, the use of machine learning algorithms is one of the best choices. This paper proposed an optimized solar radiation forecasting ensemble model consisting of pre-processing and training ensemble phases. The training ensemble phase works on an advanced sine cosine algorithm (ASCA) using Newton’s laws of gravity and motion for objects (agents). ASCA uses sine and cosine functions to update the agent’s position/velocity components by considering its mass. The training ensemble model is then developed using the k-nearest neighbors (KNN) regression. The performance of the proposed ensemble model is measured using a dataset from Kaggle (Solar Radiation Prediction, Task from NASA Hackathon). The proposed ASCA algorithm is evaluated in comparison with the Particle Swarm Optimizer (PSO), Whale Optimization Algorithm (WOA), Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), Squirrel Search Algorithm (SSA), Harris Hawks Optimization (HHO), Hybrid Greedy Sine Cosine Algorithm with Differential Evolution (HGSCADE), Hybrid Modified Sine Cosine Algorithm with Cuckoo Search Algorithm (HMSCACSA), Marine Predators Algorithm (MPA), Chimp Optimization Algorithm (ChOA), and Slime Mould Algorithm (SMA). Obtained results of the proposed ensemble model are compared with those of state-of-the-art models, and significant superiority of the proposed ensemble model is confirmed using statistical analysis such as ANOVA and Wilcoxon’s rank-sum tests.

Highlights

During the last few decades, an increase in the demand for energy resources results in finding new means of generating energy
Meteorological data from the HI-SEAS dataset (Solar Radiation Prediction) [55], [56] is randomly divided into two parts where 80% data is used for training and 20% is used for testing
DIRECTION This paper forecasts solar radiation based on a proposed advanced sine cosine algorithm-based ensemble model

Summary

Introduction

During the last few decades, an increase in the demand for energy resources results in finding new means of generating energy. Most of the ML algorithms work on the principles of predicting upcoming results based on historical data. This work uses meteorological data from the HI-SEAS (Hawai’i Space Exploration Analog and Simulation), a dataset from Kaggle (Solar Radiation Prediction, Task from NASA Hackathon). It is a dataset of weather stations for the four months (September through December 2016) between Mission IV and Mission V [55], [56]. The dataset contains different meteorological parameters such as radiations, temperature, pressure, etc. It includes static analysis of the dataset’s attributes

Results

Discussion

Conclusion