A novel optimization algorithm for energy management systems enhances cost savings and efficiency of smart buildings integrated with renewable energy sources.
The increasing complexity of energy consumption patterns has spurred research efforts aimed at enhancing energy management systems (EMS) within smart buildings. A significant advancement has been introduced through the development of the Modified Weighted Mean of Vectors (MINFO) algorithm, which promises greater efficiency and cost-effectiveness by optimizing the scheduling of varied energy sources such as diesel generators and photovoltaic (PV) systems.
With global energy consumption rising sharply from 13,277 billion kWh in 2000 to 22,347 billion kWh by 2017, as noted by the U.S. Energy Information Administration, there is mounting pressure on residential energy systems to evolve. The residential sector alone accounts for approximately 13-37% of total energy demand, making it imperative to adopt intelligent management strategies for energy consumption. The shift toward smart home automation systems allows for improved control over energy use, thereby stabilizing electrical systems.
The proposed MINFO algorithm operates based on two innovative enhancements: the Elite Centroid Quasi-Oppositional Base Learning (ECQOBL) and the Adaptive Levy Flight Motion (ALFM). These modifications aim to tackle the limitations of traditional optimization methods often encountered when dealing with nonlinear energy management problems. By enhancing the exploitation and exploration capabilities of existing algorithms, MINFO aims to significantly reduce energy management costs and improve overall efficiency.
The effectiveness of the MINFO algorithm is underscored by substantial reductions achieved during its application. Studies revealed a remarkable 53.20% decrease in electricity expenses and 53.19% reduction of the peak-to-average ratio (PAR) when compared to conventional practices. According to the authors, "the proposed MINFO is an efficient optimization method for solving the energy management solution."
These findings not only demonstrate MINFO's robustness but also its practicality for real-world applications, establishing it as a front-runner among energy management strategies.
Further insights from the research indicate MINFO's superior performance compared to different optimization methods tested across various benchmark functions, showcasing stable metrics of global searching accuracy. The researchers concluded, "the MINFO has stable performance and the best global searching accuracy, which have been demonstrated via convergence trends." Such performance metrics highlight the algorithm's ability to adapt and provide consistent energy management solutions.
For application within smart buildings, MINFO revolutionizes energy management, optimizing multiple energy sources effectively. The study exemplifies its capabilities by achieving targeted reductions of costs and PAR values, which are integral to enhancing energy efficiency. Specifically, the algorithm managed to lower energy bills from 169.96€ to 79.55€, benefiting from time-of-use-based demand side response (DSR) strategies, demonstrating the need for advanced algorithms to adapt to dynamic energy pricing.
Though MINFO showcases significant improvements, the study also notes areas for future enhancement. Potential avenues of investigation include incorporating uncertainty factors related to renewable energy sources and variable load demands, reflecting the complexity of real-life energy challenges households face.
The transition to smart energy management solutions like MINFO marks progress in response to the ever-changing residential energy demands and sustainability goals. Its application reinforces the significance of integrating intelligent energy solutions to promote economic viability and environmental responsibility. The availability of efficient energy systems is key to fostering sustainability, and MINFO's development leads the way toward greener energy consumption practices.