1 Artificial intelligence and Internet of things for renewable energy systems

Abstract

The sustainability incredibly insists in having innovations in renewable energy. To obtain an unsullied and a well-grounded environment, innovations in the present mechanisms have to be uplifted ensuring a predictive framework and an enormous outcome can be expected. The goal needs to be met to establish new research concepts and to move on the energy requirements in optimization of the existing machine learning (ML) framework. With the lot more renewable energy existing in nature, the two most variable and commonly used renewable energies are solar and wind. Its cons are nonuniformity of power and its dependency on external environmental factors. Due to this, sole dependence on renewable energy is not possible, and hence conventional power grid is also to be considered when any sort of predictive analysis needs to be done. Hence, more concentration is to be made on forecasting of renewable energy and on smart grids ensuring continuous equilibrium and balance within renewable energy and conventional grid. The electricity demand and supply of power can be predicted using ML algorithms ensuring better savings with operational costs. The two-way electricity and information flow will prove smart grid in future ensuring continuous monitoring of the network bringing more requirements for ML framework. The early warning systems incorporated by Germany symbolize a very good example of how ML algorithms analyze the realtime data from various renewable energy sources to analyze the total amount of energy requirement of the country. Google invention on DeepMind proves that the energy efficiency is 3.5 times more compared to the energy demands of the last 5 years. The innovations on intelligent home energy management systems prove promising energy usage with ML in real time. The consumer or the customer behavior can be predicted easily with intelligent techniques along with various other features like weather or climate modeling ensuring a complete and interoperable framework that suits the conventional grids. Artificial intelligence helps in sustainability of the grid with more focus on demand response. The energy management and operation cost management in smart grid is a promising feature incorporating ML algorithms in renewable energy. The optimization on managing the asset along with its maintenance ensures efficient management of power. The large amount of data collected brings data mining and prediction, thereby enabling more analytics on data. To establish such a kind of platform, various transfer models for solar and wind need to be applied along with irradiance to power models incorporating blending of information along with its categorization in ML algorithms. Feature selection methods along with the diversified algorithms in ML for diversified applications ensure continuous upgradation in framework rather than integrated approach.