Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities
The energy industry is at a crossroads. Digital technological developments have the
potential to change our energy supply, trade, and consumption dramatically. The new …
potential to change our energy supply, trade, and consumption dramatically. The new …
Machine learning technology in biodiesel research: A review
M Aghbashlo, W Peng, M Tabatabaei… - Progress in Energy and …, 2021 - Elsevier
Biodiesel has the potential to significantly contribute to making transportation fuels more
sustainable. Due to the complexity and nonlinearity of processes for biodiesel production …
sustainable. Due to the complexity and nonlinearity of processes for biodiesel production …
Optimal planning of solar photovoltaic and battery storage systems for grid-connected residential sector: Review, challenges and new perspectives
Integration of solar photovoltaic (PV) and battery storage systems is an upward trend for
residential sector to achieve major targets like minimizing the electricity bill, grid …
residential sector to achieve major targets like minimizing the electricity bill, grid …
Application of Artificial Neural Networks to photovoltaic fault detection and diagnosis: A review
The rapid development of photovoltaic (PV) technology and the growing number and size of
PV power plants require increasingly efficient and intelligent health monitoring strategies to …
PV power plants require increasingly efficient and intelligent health monitoring strategies to …
Application of big data and machine learning in smart grid, and associated security concerns: A review
This paper conducts a comprehensive study on the application of big data and machine
learning in the electrical power grid introduced through the emergence of the next …
learning in the electrical power grid introduced through the emergence of the next …
A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models
The uncertainty associated with modeling and performance prediction of solar photovoltaic
systems could be easily and efficiently solved by artificial intelligence techniques. During the …
systems could be easily and efficiently solved by artificial intelligence techniques. During the …
Machine learning methods for solar radiation forecasting: A review
Forecasting the output power of solar systems is required for the good operation of the
power grid or for the optimal management of the energy fluxes occurring into the solar …
power grid or for the optimal management of the energy fluxes occurring into the solar …
Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques
The modernisation of the world has significantly reduced the prime sources of energy such
as coal, diesel and gas. Thus, alternative energy sources based on renewable energy have …
as coal, diesel and gas. Thus, alternative energy sources based on renewable energy have …
A comparison of fuzzy DEA and fuzzy TOPSIS in sustainable supplier selection: Implications for sourcing strategy
K Rashidi, K Cullinane - Expert Systems with Applications, 2019 - Elsevier
This paper presents a comparative analysis of the outcomes achieved when two widely
applied methods for supplier selection—the 'technique for order of preference by similarity to …
applied methods for supplier selection—the 'technique for order of preference by similarity to …
A survey of the most used MPPT methods: Conventional and advanced algorithms applied for photovoltaic systems
Abstract Maximum Power Point Tracking (MPPT) methods are used in photovoltaic (PV)
systems to continually maximize the PV array output power which generally depends on …
systems to continually maximize the PV array output power which generally depends on …