Evaluation of the new model of renewable energy economic growth under the background of big data

Abstract In the era of Big Data (BD), available data resources have become more abundant, and data analysis technologies have matured significantly. The application of BD technology has, to some extent, accelerated economic development, meanwhile the use of renewable energy (RE) has been playing an increasingly important role on promoting economic growth. Based on the current state of renewable energy (RE) development in China, this paper analyzes the significance of RE development and evaluates its impact on economic growth. It integrates a time-series production simulation method with power engineering principles, conducts testing and analysis of RE. Experimental results show that, in terms of RE absorption capacity, the highest absorption value achieved by the proposed algorithm is 1582 MW, compared to 1336 MW for the traditional algorithm (RE t ). Regarding the amount of abandoned RE, the highest value under the proposed algorithm is 475 MW, whereas the traditional algorithm reaches 639 MW. In terms of the proportion of abandoned RE, the proposed algorithm yields a highest share of 8.32%, significantly lower than the 14.32% observed in the traditional algorithm (RE t ). In summary, the proposed algorithm enhances effectively RE absorption capacity and reduces the proportion of abandoned RE, thereby increasing RE output. From a power engineering perspective, improving RE utilization efficiency contributes to more stable grid operation, reduces the need for backup power from fossil sources, and lowers system integration costs. Therefore, a vigorous development of RE to promote the optimization and upgrading of energy and related industrial structures is essential for improving the ecological environment and ensuring sustainable economic growth.