In the quest for sustainable development, buildings stand as both a challenge and an opportunity. They account for a significant slice of global energy consumption, making them a prime target for optimization. Enter Jie Gong, a researcher from the School of Construction Management at Chongqing Metropolitan College of Science and Technology, who has proposed a novel approach to building energy management that could reshape the energy sector’s commercial landscape.
Gong’s research, published in the *International Journal of Renewable Energy Development* (translated as *Journal of Renewable Energy Development*), integrates a rule-based control algorithm with a genetic algorithm to create a building energy management model that promises to reduce operating costs and optimize energy utilization. “The idea is to provide a more intelligent, adaptive system for managing building energy,” Gong explains. “By combining the strengths of both algorithms, we can tackle the complexity and uncertainty of energy optimization problems more effectively.”
The rule-based control algorithm serves as the decision-support backbone, providing a set of predefined rules for managing different devices within the building energy system. The genetic algorithm, on the other hand, brings a touch of evolutionary biology to the table, using processes akin to natural selection to optimize energy usage. “The genetic algorithm helps us navigate the vast solution space, finding optimal or near-optimal solutions that a rule-based system alone might miss,” Gong adds.
The results speak for themselves. Comparative tests showed that the proposed fusion algorithm had higher fitness values and faster convergence speed than other algorithms. Moreover, the model demonstrated significant cost savings. For two types of buildings, the energy expenditures were reduced to 788.3 yuan and 967.6 yuan, respectively. Taking Building 1 as an example, the proposed model reduced the cost of energy consumption optimization by 39.30% compared to Supervisory Control and Data Acquisition (SCADA), 28.32% compared to Beetle Antennae Search and Particle Swarm Optimization (BAS-PSO) algorithm, and 20.20% compared to Long Short-Term Memory-Convolutional Neural Network (LSTM-CNN) algorithm.
The implications for the energy sector are substantial. “This research could lead to more efficient energy management systems, reducing costs for building owners and operators while also contributing to global sustainability goals,” says Gong. The model’s ability to integrate with existing systems and its adaptability to different building types make it a versatile tool for the energy sector.
As we look to the future, Gong’s research offers a glimpse into a world where buildings are not just energy consumers but also smart energy managers. “The potential is enormous,” Gong concludes. “With further refinement and real-world testing, this model could become a standard tool in building energy management, helping us build a more sustainable future.”
In an industry where every yuan saved is a step towards sustainability, Gong’s research is a beacon of innovation, guiding the energy sector towards a more efficient and cost-effective future.