Abstract
AbstractWith the continuous advancement of urbanization, the contradiction between urban development and environmental resources has become increasingly prominent, and environmental pollution has become increasingly serious. To fundamentally solve the problems of environment, energy, and low carbon, we must rely on the intelligence of energy. This paper aims to study the sustainable development of China's intelligent energy industry based on artificial intelligence and low‐carbon economy. In view of the problems existing in the optimization of power generation industry, this paper uses the annual load, electricity price, weather, and climate data of a southern power grid, uses the statistical variation particle swarm optimization algorithm, uses the historical runoff and rainfall data to optimize it, and studies the analysis methods, characteristics and laws of short‐term load, electricity price and runoff, as well as the uncertain factors affecting their changes. The experimental results show that the predicted price is close to the actual price, and the median error of each period is <1% in statistical analysis, so the forecast value can be used to replace the actual value for scheduling. This method makes full use of the adaptive mutation in the late stage of particle optimization, and introduces the mechanism of particle size selection, which fully ensures the diversity of particles and improves the search ability of particles.
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call