Industrial power multi-dimensional aggregation and peak-valley difference analysis method

Abstract

This invention discloses a method for multi-dimensional aggregation and peak-valley difference analysis of industrial electricity consumption. This method aggregates and statistically analyzes electricity consumption data across multiple dimensions, including date, time period, high-voltage room, and production line, and automatically calculates peak-valley differences, enabling in-depth analysis of electricity consumption data. This invention solves the problems of existing analyses being limited to a single dimension and unable to quantify energy consumption differences. It is suitable for refined energy consumption management in industrial enterprises and can effectively support energy cost optimization decisions. This invention has been practically implemented in state-owned steel plants for many years.

Description

Technical Field

[0001] This invention relates to the field of industrial data analysis technology, specifically to a statistical analysis method for electricity data, and more particularly to a multi-dimensional aggregation and peak-valley difference analysis method suitable for industrial energy consumption management systems. Background Technology

[0002] Industrial enterprises have complex energy consumption structures, requiring statistical analysis of electricity consumption data from multiple dimensions. Existing electricity consumption analysis methods have the following shortcomings: The analysis is limited to a single dimension: statistics are mostly based on time or a single region, lacking multi-dimensional hierarchical statistics such as "high-pressure room", "production line type" and "time period type", making it impossible to gain a deep understanding of the energy consumption characteristics of each unit.

[0003] Lack of peak-valley difference quantification: Only the total electricity consumption for each time period is counted, and the difference between "valley electricity consumption" and "peak electricity consumption" is not quantitatively analyzed, which cannot intuitively reflect the differences in energy consumption costs and losses caused by time period differences.

[0004] Fragmented production line aggregation: There is a lack of integrated design for the aggregation and analysis of the total power consumption of the high-pressure chamber and the power consumption of subordinate production lines (such as pickling and rolling lines and leveling machines), the data correlation is low, and it is impossible to conduct an overall energy consumption assessment. Summary of the Invention

[0005] The purpose of this invention is to provide a method for multi-dimensional aggregation and peak-valley difference analysis of industrial electricity consumption, so as to achieve refined and multi-dimensional analysis of electricity data and quantify the difference in peak and valley energy consumption.

[0006] This invention discloses a method for multi-dimensional aggregation and peak-valley difference analysis of industrial electricity, comprising the following steps: Multi-dimensional data aggregation: Using date and peak / off-peak periods as the core dimensions, the power consumption of each high-pressure chamber, such as the pickling and rolling high-pressure chamber, the cold galvanizing high-pressure chamber, and the pickling high-pressure chamber in the east area, is aggregated in layers; at the same time, the power consumption of each production line, such as the pickling and rolling line, the leveling machine, and the degreasing line, is aggregated independently.

[0007] Peak-valley difference calculation: For each aggregation unit, the difference between "valley power consumption" and "peak power consumption" is automatically calculated, i.e., peak-valley difference, to quantify the energy consumption differences in different time periods.

[0008] Overall summary analysis: The power consumption of each high-voltage room and each production line at different times is summarized to generate a complete energy consumption analysis chart and calculate the relationship between the total incoming power consumption and the total production line power consumption.

[0009] Output results: The output includes analytical reports containing multi-dimensional aggregated data, peak-valley difference data, and production line summary data, providing a basis for decision-making in energy consumption optimization and cost control.

Claims

1. A method for multi-dimensional aggregation analysis of industrial electricity, characterized in that, Includes the following steps: (1) Perform hierarchical aggregation calculations on the power data of each high-voltage room according to two core dimensions: date and peak / off-peak time period; (2) The electricity data of each production line is independently aggregated and calculated according to two core dimensions: date and peak / off-peak time period; (3) For each aggregation unit, the difference between the power consumption in the valley segment and the power consumption in the peak segment is automatically calculated, i.e., the peak-valley difference.

2. The method according to claim 1, characterized in that, The high-pressure chambers include a pickling and rolling high-pressure chamber, a cold galvanizing high-pressure chamber, a hot-dip galvanizing high-pressure chamber, and an eastern pickling high-pressure chamber; the production line includes a pickling and rolling line, a leveling machine, a degreasing line, and a bell-type furnace.

3. The method according to claim 1, characterized in that, The analysis method also includes calculating the difference between the total incoming power and the total power of each sub-production line to assess overall energy consumption.