This paper proposes a new hybrid framework for short-term load forecasting (STLF) by combining the Feature Engineering (FE) and Bayesian Optimization (BO) algorithms with a Bayesian Neural Network (BNN). The FE module comprises feature selection and extraction phases. Firstly, by merging the Random Forest (RaF) and Relief-F (ReF) algorithms, we developed a hybrid feature selector based on grey correlation analysis (GCA) to eliminate feature redundancy. Secondly, a radial basis Kernel function and principal component analysis (KPCA) are integrated into the feature-extraction module for dimensional reduction. Thirdly, the Bayesian Optimization (BO) algorithm is used to fine-tune the control parameters of a BNN and provides more accurate results by avoiding the optimal local trapping. The proposed FE-BNN-BO framework works in such a way to ensure stability, convergence, and accuracy. The proposed FE-BNN-BO model is tested on the hourly load data obtained from the PJM, USA, electricity market. In addition, the simulation results are also compared with other benchmark models such as Bi-Level, long short-term memory (LSTM), an accurate and fast convergence-based ANN (ANN-AFC), and a mutual-information-based ANN (ANN-MI). The results show that the proposed model has significantly improved the accuracy with a fast convergence rate and reduced the mean absolute percent error (MAPE).

The study provides a numerical analysis of laminar forced convective heat transfer of graphene oxide nanosheets suspended in the mixture of water and ethylene glycol in laminar flow regime using single phase approach in a circular horizontal tube under constant heat flux conditions. The length and diameter of the simulation domain for numerical investigation are 2 m and 4.5 mm respectively. The effect of various flow conditions and weight concentrations have been investigated on local heat transfer coefficient, average heat transfer coefficient, friction factor, pressure drop and thermal performance factor of the nanofluids. The range of weight concentration and Reynolds number used in this study are 0.01–0.1 wt.% and 400–2000 respectively. The maximum percentage enhancement in average heat transfer coefficient was 13.04% for weight concentration and Reynolds number of 0.1 wt.% and 2000 respectively. The maximum pressure drop enhancement ratio was 2.12 at Reynolds number and weight concentration of 400 and 0.10 wt.%. The enhancement in heat transfer coefficient was found lower as compared to corresponding enhanced pressure drop for all weight concentrations. The thermal performance factor of nanofluids is less than one for all weight concentrations and nanofluids showed no advantage over base fluid as heat transfer fluid in laminar flow regime.