KEYWORDS: Fermium, Frequency modulation, Mining, Databases, Feature extraction, Air temperature, Matrices, System integration, Data modeling, Data processing
The big data processing technology plays a key role in investigating the integrated energy system and developing a detailed information database, which has potential to enhance the efficiency of energy utilization. In this paper, a novel data structure, the Frequent Items Matrix (FM), is introduced, which efficiently compresses high-dimensional sparse databases and reduces the computational load for support counting. Leveraging the FM structure, the FM-growth algorithm for frequent itemset mining is developed. The FM-growth algorithm requires only two scans of the transaction database and computes all frequent item sets using straightforward matrix operations, thereby minimizing the generation of intermediate results. In the case study, the optimized target values determined using the proposed method exhibit a consistent overall trend with the design values, which verifies the efficacy and potential of the proposed method.
KEYWORDS: Data modeling, Solar energy, Control systems, Data storage, Data centers, Data acquisition, Information fusion, Inspection equipment, Intelligence systems, Distributed computing
The development of distributed energy technologies has accelerated the cross-fertilization of different energy systems and has brought new challenges to the development of the energy internet and energy management. The core of current energy development is efficient energy use and low carbon environmental protection. To achieve efficient energy use, this paper analyzes the level of intelligent control of distributed energy devices based on big data feature mining technology to enhance the autonomy of individual energy supply and energy use and the synergy of the system to achieve the purpose of decentralization. However, the existing data processing work lacks data quality evaluation standards that are unified and deeply integrated with professional management, and the quality and validity of the stock and incremental basic data cannot be assessed comprehensively. To this end, we propose and build an evaluation system for intelligent control level of distributed energy equipment, and through the establishment of a data quality evaluation system to conduct comprehensive analysis and evaluation of distributed equipment basic data, we realize effective control of basic data status and make data governance work more intelligent.
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