Prediction of solar energy guided by pearson correlation using machine learning

Application of novel framework based on ensemble boosted regression trees and Gaussian process regression in modelling thermal performance of small-scale organic rankine cycle using hybrid nanofluid

https://irojournals.com/jscp/V3/I2/04.pdf

A Self Monitoring and Analyzing System for Solar Power Station using IoT and Data Mining Algorithms

Photovoltaic power forecasting based on GA improved Bi-LSTM in microgrid without meteorological information

Short-term multi-energy load forecasting for integrated energy systems based on CNN-BiGRU optimized by attention mechanism

Article history: Received: 10 November, 2020 Accepted: 06 January, 2021 Online: 22 January, 2021

Deep Learning based Models for Solar Energy Prediction

Recently, the majority of countries are seeking to redefine their energy mix, due to the global warming and climate change concerns. As a result, the world is moving towards the use of renewable energy to produce electricity. In this sense, solar energy has become one of the main strengths of the most promising renewable resources for mass power generation. Solar energy forecasting is a very active field of research. Its objective is to reduce the uncertainty of variable renewable production. Statistical, physical, machine learning and hybrid approaches were used. However, research contributions in this area remain immature. Therefore, the search for more relevant methods, adapted to this field, will be important because it would improve accuracy, in order to provide the world with many economic and environmental benefits. In this context, this article discusses the fundamental principles of the machine learning based approaches.

The forecasting of solar energy based on Machine Learning

Coronavirus 2019 (COVID-19) has reached the stage of an international epidemic with a major socioeconomic negative impact Considering the weakness of the healthy structure and the limited availability of test kits, particularly in emerging countries, predicting the spread of COVID-19 is expected to help decision-makers to improve health management and contribute to alleviating the related risks In this article, we studied the effectiveness of machine learning techniques using Morocco as a case-study We studied the performance of six multi-step models derived from both Machine Learning and Deep Learning regards multiple scenarios by combining different time lags and three COVID-19 datasets(periods): confinement, deconfinement, and hybrid datasets The results prove the efficiency of Deep Learning models and identify the best combinations of these models and the time lags enabling good predictions of new cases The results also show that the prediction of the spread of COVID-19 is a context sensitive problem © 2021 International Information and Engineering Technology Association All rights reserved

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Imane Jebli

Papers

Prediction of solar energy guided by pearson correlation using machine learning

Deep Learning based Models for Solar Energy Prediction

The forecasting of solar energy based on Machine Learning

Artificial intelligence-enabled and period-aware forecasting COVID-19 spread