This paper presents a comprehensive review of machine learning (ML) based approaches, especially artificial neural networks (ANNs) in time series data prediction problems. According to literature, around 80% of the world’s total energy demand is supplied either through fuel-based sources such as oil, gas, and coal or through nuclear-based sources. Literature also shows that a shortage of fossil fuels is inevitable and the world will face this problem sooner or later. Moreover, the remote and rural areas that suffer from not being able to reach traditional grid power electricity need alternative sources of energy. A “hybrid-renewable-energy system” (HRES) involving different renewable resources can be used to supply sustainable power in these areas. The uncertain nature of renewable energy resources and the intelligent ability of the neural network approach to process complex time series inputs have inspired the use of ANN methods in renewable energy forecasting. Thus, this study aims to study the different data driven models of ANN approaches that can provide accurate predictions of renewable energy, like solar, wind, or hydro-power generation. Various refinement architectures of neural networks, such as “multi-layer perception” (MLP), “recurrent-neural network” (RNN), and “convolutional-neural network” (CNN), as well as “long-short-term memory” (LSTM) models, have been offered in the applications of renewable energy forecasting. These models are able to perform short-term time-series prediction in renewable energy sources and to use prior information that influences its value in future prediction.

In this paper, artificial neural network (ANN) based Levenberg-Marquardt (LM), Bayesian Regularization (BR) and Scaled Conjugate Gradient (SCG) algorithms are deployed in maximum power point tracking (MPPT) energy harvesting in solar photovoltaic (PV) system to forge a comparative performance analysis of the three different algorithms. A comparative analysis among the algorithms in terms of the performance of handling the trained dataset is presented. The MATLAB/Simulink environment is used to design the maximum power point tracking energy harvesting system and the artificial neural network toolbox is utilized to analyze the developed model. The proposed model is trained with 1000 dataset of solar irradiance, temperature, and voltages. Seventy percent data is used for training, while 15% data is employed for validation, and 15% data is utilized for testing. The trained datasets error histogram represents zero error in the training, validation, and test phase of data matching. The best validation performance is attained at 1000 epochs with nearly zero mean squared error where the trained data set is converged to the best training results. According to the results, the regression and gradient are 1, 1, 0.99 and 0.000078, 0.0000015739 and 0.26139 for Levenberg-Marquardt, Bayesian Regularization and Scaled Conjugate Gradient algorithms, respectively. The momentum parameters are 0.0000001 and 50000 for Levenberg-Marquardt and Bayesian Regularization algorithms, respectively, while the Scaled Conjugate Gradient algorithm does not have any momentum parameter. The Scaled Conjugate Gradient algorithm exhibit better performance compared to Levenberg-Marquardt and Bayesian Regularization algorithms. However, considering the dataset training, the correlation between input-output and error, the Levenberg-Marquardt algorithm performs better.

Extinction of fossil fuel resources owing to excessive use has called up new solution-based resources called ‘biomass’ which is more affordable to deliver and utilize, bring less environmental damages such as greenhouse gas (GHG) emission, opened more employment opportunities in both rural or suburban areas due to the abundant availability of this resources throughout the world. About 44% of the energy required by the year 2030 and about 14% energy sourced from biomass in 2016 based on the World Bioenergy Association’s data. Biomass can be sorted from various sources such as municipal solid waste (MSW), agricultural crops, crop residues, and forest residues. The clear cut of biomass characteristics in proximate analysis and ultimate analysis makes it possible to produce reliable energy resources. Biomass is multi-faceted fuel; thereof, it produces biofuel, which can be used in transportation and bioenergy to generate cleaner and affordable electricity throughout the world. In that sense, in this article, the characteristics and functionality of different thermochemical processes such as direct combustion and gasification have been discussed, and the reliability and new findings in various aspects in the bioenergy field. Furthermore, the advantages and disadvantages have been discussed to substantiate further the core objective of renewable energy in producing better, cheaper, and harmless to the surrounding environment. This study will give a brief understanding of the right way of using biomass and indirectly reduce the usage of fossil fuel, thus reserving a better world for the future generation.