The rapid change and development in human life, information technology, and the increase in using home gadgets, modern appliances, and electric cars, leads to more dependency on electrical resources and consecutive increase in CO2 emission from generation plant. The current world issue is on how to save the energy by reducing the consumption and decreasing global warming. In this research, Smart Home Energy Management System (SHEMS) has been developed to operate home appliances in an optimum approach. It is aimed at reducing the consumption energy by detecting the residents' activity and identifying it among three states: Active, Away, or Sleep. The SHEMS is designed with an algorithm that is based on Hidden Markov Model (HMM) in order to estimate the probability of the home being in each of the above states. The proposed system uses the WiFi technology for data transmission inside home and the GSM technology for external communication. The proposed system and its algorithm was successfully tested and 18% of energy saving were obtained.

This work experimentally and numerically explored how varied steel-polypropylene fibre mixtures affected simply supported reinforced concrete deep beams. Due to their better mechanical qualities and durability, fibre-reinforced polymer composites are becoming more popular in construction, with hybrid polymer-reinforced concrete (HPRC) promising to increase the strength and ductility of reinforced concrete structures. The study evaluated how different combinations of steel fibres (SF) and polypropylene fibres (PPF) affected beam behaviour experimentally and numerically. The study’s focus on deep beams, research of fibre combinations and percentages, and integration of experimental and numerical analysis provide unique insights. The two experimental deep beams were the same size and were composed of hybrid polymer concrete or normal concrete without fibres. Fibres increased deep beam strength and ductility in experiments. The calibrated concrete damage plasticity model in ABAQUS was used to numerically calibrate HPRC deep beams with different fibre combinations at varied percentages. Based on six experimental concrete mixtures, calibrated numerical models of deep beams with different material combinations were investigated. The numerical analysis confirmed that fibres increased deep beam strength and ductility. HPRC deep beams with fibre performed better than those without fibres in numerical analysis. The study also determined the best fibre percentage to improve deep beam behaviour where a combination of 0.75% SF and 0.25% PPF was recommended to enhance load-bearing capacity and crack distribution, while a higher content of PPF was suggested for reducing deflection.

Twelve simple span reinforced concrete deep beams were tested under symmetrically two points top load to examine the effect of steel fiber and polypropylene fiber and influence of the transverse circular openings on their behavior. The variables investigated involve beams with and without openings, the volume fraction of fibers, shear span to effective ratio a/d, and inclined reinforcement around the openings. All the beams had the same overall dimensions, flexural reinforcement and opening size. Many mixes have been used by combination between steel fibers and polypropylene fibers with different percentages of (1%SF-0%PPF), (0.75%SF-0.25%PPF), (0.5%SF-0.5%PPF), (0.25%SF-0.75%PPF), (0%SF-1%PPF) in addition to mix without fibers. The test results showed that fibers greatly increase the diagonal cracking strength and shear strength of reinforced concrete deep beams, where the variation of the type of concrete from normal concrete to hybrid concrete for deep beam contains openings led to increase the ultimate strength by about 30%. In addition, it was found that the shear capacity of deep beams increased when the shear-span/effective depth ratio decreased. The inclined reinforcement around the opening was observed to be very efficient in improving the ultimate load capacity and deflection response.

This study proposed a system model through which reserve and energy capacity could be dispatched. Moderating factors that were considered included unsteady power generation and uncertain load demand. The parameters considered centered on uncertainty in power demand management while considering environmental, user, and utility objectives. The proposed mode also considered day-ahead markets for electricity relative to power demand bid variation. In so doing, the model strived to generate certain amounts of the required energy, as well as reserve capacity. Furthermore, the model predicted any lost opportunity cost before incorporating the expected load that would go unreserved. The investigation culminated into the analysis of the impact of separate and combined energy dispatch and reserve on system outcomes. To address non linear cost curves, robust optimization technique was used to optimize the selected objective function. To conduct and evaluate the numerical outcomes, as well as the feasibility of the proposed framework, case analyses were conducted. From the simulation outcomes, the proposed scheduling model proved effective in such a way that it posed beneficial effects such as improved system stability and reduced cost.