Prediction of tubular solar still performance by machine learning integrated with Bayesian optimization algorithm

Performance Prediction and Optimization of Solar Water Heater via a Knowledge-Based Machine Learning Method

Measuring the performance of solar energy and heat transfer systems requires a lot of time, economic cost and manpower. Meanwhile, directly predicting their performance is challenging due to the complicated internal structures. Fortunately, a knowledge-based machine learning method can provide a promising prediction and optimization strategy for the performance of energy systems. In this Chapte...

Experimental Study on a Compound Parabolic Concentrator Tubular Solar Still Tied with Pyramid Solar Still

Practical Bayesian Optimization of Machine Learning Algorithms

The use of machine learning algorithms frequently involves careful tuning of learning parameters and model hyperparameters. Unfortunately, this tuning is often a “black art” requiring expert experience, rules of thumb, or sometimes bruteforce search. There is therefore great appeal for automatic approaches that can optimize the performance of any given learning algorithm to the problem at hand....

Comparative Analysis of Machine Learning Algorithms with Optimization Purposes

The field of optimization and machine learning are increasingly interplayed and optimization in different problems leads to the use of machine learning approaches‎. ‎Machine learning algorithms work in reasonable computational time for specific classes of problems and have important role in extracting knowledge from large amount of data‎. ‎In this paper‎, ‎a methodology has been employed to opt...

design, fabrication and performance evaluation of a tubular vertical solar still with an absorber of porous silicon carbide

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