Data-Driven Approach for the Short-Term Business Climate Forecasting Based on Power Consumption. (27th April 2022)

• Record Type: Journal Article
• Authors: Xu, Ji; Zhou, Hong; Fang, Yanjun; Liu, Lan
• Other Names: Qureshi Nawab Muhammad Faseeh Academic Editor.
• Is Part Of: Wireless communications and mobile computing. Volume 2022(2022)
• Publication Date: 2022-04-27
• Subjects: Wireless communication systems -- Periodicals; Mobile communication systems -- Periodicals; 621.38205
• DOI: 10.1155/2022/4037053
• Languages: English
• ISSNs: 1530-8669

Abstract

Abstract : With the fast development of intelligent data-mining technologies, some advanced artificial intelligence approaches are widely developed and employed to help the decision-making of enterprises and government. The application of advanced and intelligent approaches successfully helps the enterprises and government find out the valuable information hidden in the massive economic data. This study presents a novel data-driven approach to forecast the short-term business climate using the electric power consumption data of large enterprises. In addition, the climate conditions, interactions between different industries, the business cycle, and some other related variables are also considered and included in the developed forecasting model. To be specific, the business climate prediction model based on support vector machine (SVM) is proposed firstly, and the human-simulated particle swarm optimization algorithm (HSPSO) in our previous work is employed to optimize the parameters of the developed forecasting model. Secondly, a novel power-consumption-based business climate index (BCI) system is developed and comprehensively analyzed. The developed BCI system that contains the index for each separate industry (BCI-I), the index for tertiary industry (BCI-T), the index for secondary industry (BCI-S), and the index for the entire province (BCI-P) is proposed. In addition, the developed BCI system is employed to normalize the output of SVM-based forecasting model to directly indicate the business climate, which is very important to the decision-making of enterprises and government under the background of smart cities. Finally, the real data of Guangdong province in China, including the gross output values (GOV) and detailed power consumptions of more than 38000 enterprises, are employed to test the proposed approach. Experimental results show that the GOV of each industry and the whole society predicted by HSPSO-SVM matches the real data well. Moreover, the predicted BCI can directly indicate the business climate in advance, which is of great value for economic-decision and policy-making of both enterprises and government.