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By integrating smart grid technology with home energy management systems, households can monitor and optimise their energy consumption. This allows for more efficient use of energy resources, reducing waste and lowering energy bills. In this manuscript, a hybrid approach is proposed for smart grid home energy management with microgrids and day-ahead energy forecasts. The proposed control approach combines the Circle Search (CS) algorithm and Recalling-Enhanced Recurrent Neural Network (RERNN). Commonly it is named as CS-RERNN technique. The novelty of this paper is to optimise energy consumption and production within microgrids, thereby contributing to the overall efficiency of the smart grid system. The proposed method is used to reduce the electricity cost, peak-to-average ratio (PAR), and maximising consumer comfort. Energy management is performed based on the CS algorithm. A smart home connected to the external power grid (PG) is managed by the proposed method. Here, load demand is predicted by using RERNN. By then, the performance of the proposed method is implemented in MATLAB platform. The proposed method shows a high efficiency of 96%, and 22 $ of low electricity bill cost compared with other existing methods such as Particle swarm optimisation (PSO), Cuckoo search algorithm (CSA, and Border collie Optimisation (BCO). [ABSTRACT FROM AUTHOR]

Aluminum metal-matrix composites (AMMCs) were prepared by dispersing TiO2 dispersoids of different volume fractions into an AA6063 matrix via stir casting and subjected to process-structure correlation studies. Four different samples based on weight ratio were considered herein: 99Al-1TiO2, 97Al-3TiO2, 95Al-5TiO2, and the as-received AA6063. Their mechanical properties namely, microhardness, tensile strength, and tribological behavior, were determined. In addition, the microstructure of the samples was also analysed. It was observed that the addition of 5% TiO2 particles enabled the AA6063 matrix to accommodate a higher strain energy while providing the required driving force to generate dislocations and substructures. Therefore, considering the plastic deformation, the ultimate tensile strength (sut) increased gradually with the addition of TiO2 (in weight%). The flow curves of the 95Al-5TiO2 sample showed the highest value of sut, whereas the as-received AA6063 matrix exhibited the lowest value. For linear elastic deformation, AA6063 showed the lowest yield strength (sys) as compared to the AMMC samples for all TiO2 weight% values; however, the variation in sys among the AMMC samples was minimal. The microhardness of the samples increased gradually with the addition of TiO2, and the percentage reduction in area at the fracture was largest for 95Al-5TiO2. The Taguchi's L9 array and variance analysis of the process parameters indicated that the material wear was largely affected by the normal load, followed by weight% of TiO2 and sliding speed. Wear surface characteristics, such as microvoids, delamination, microcracks, and wear debris, were qualitatively observed in all the AMMC samples. The overall strength improvement was attributable to the effects of addition of the dispersoids. During melt solidification, the TiO2 particles surpassed/pinned and hindered the grain growth, resulting in grainsize refinement. [ABSTRACT FROM AUTHOR]