祝贺Tanveer Ahmad同学的论文《Effective bulk energy consumption control and management for power utilities using artificial intelligence techniques under conventional and renewable energy resources》被《International Journal of Electrical Power and Energy Systems》期刊录用。
论文题目：Effective bulk energy consumption control and management for power utilities using artificial intelligence techniques under conventional and renewable energy resources
作者: Tanveer Ahmad, Huanxin Chen
第一单位：Department of Refrigeration and Cryogenics, Huazhong University of Science and Technology, Wuhan, China
期刊名：International Journal of Electrical Power and Energy Systems
Increasing sustainability demands initiateestimating various design and control opportunities for classifyingenergy-efficient plan ever more significant. These conditions demand simulationalgorithms which are not only fast but also accurate. Artificial intelligence(AI) enables efficient mimicry of bulk energy consumption control whileproducing results much faster than data-mining and machine learning models.This study proposes two AI based approaches for utilities bulk energyconsumption prediction, control and management. Two different zones actualenvironmental and energy consumption data are obtained for input featuresselection and modeling analysis. Each zone is categorized into five featuresparameter selection (PS) states. Each PS state is further divided into fourdifferent hidden neurons (HD) and hidden layers of the model’s network. Theforecasting duration is based on 1-month and 1-year ahead intervals formedium-term (MT) and long-term (LT) respectively. Further the current proposedmodel’s performance is compared with three existing models. One of thepromising finding in this research is that substantial improvement inprediction accuracy applying features extracted by PS-3 and PS-5. Results showthat AI models are powerful in solving complex and nonlinear patterns of rawdata. This study renders optimal decisions can be projected while utilitiesenergy supply strategy & control, capacity expansion, capital investmentresearch market management, revenue analysis and future load requirement forecasting.
Fig. 1. ACF and PACF of net energy usage ofZone-1 and Zone-2.
Fig.2. Proposed forecasting methodology
Fig. 4. Aggregated actual and forecastedenergy requirement for long-term load management.
Fig. 5. The gradient of PRGBNNs and GDALBNNs models.
Fig. 6. Forecasting results compared withthe existing models.
Tanveer Ahmad (韩珂)