Data Warehousing and Analysis for Smart Electric Meters
Author(s):
Radhika Jayant Gogate , KLS Gogte Institute of Technology , Belgaum
Keywords:
GSM Module, PSoC Kit, Engine Locking, Sos Message, Smart System
Abstract:
A data warehouse is a relational/multidimensional database that is designed for query and analysis It usually contains historical data that is derived from transaction data. Data Warehouse is an important asset for organizations to maintain efficiency, profitability and competitive advantages. In an Organizations data from various source systems is collected which has degree of value and business relevance. Data Warehouses requires a blend of business intuitiveness and technical skills. As data warehouse design is the one time process all future requirements and expansion have to be predicted in advance to design it.Data Warehousing is a leading technology for data processing and centrally maintaining it for analysis, report generation and decision making in industries and organizations. This paper presents a methodology to build a data warehouse for Smart Electric Meters and then extract and analyze various parameters. The proposed system will work as follows:The meter reading process will be automated eliminating need to send someone personally for reading the meter which will ensure more accurate billing. Electricity usage and related factors of each consumer will be noted by the meter at frequency of 30 minutes which gives total 48 readings for each consumer per day. This daily file will be sent to the server for further processing. Pricing of Electricity will be done differently for peak and off-peak hours by lowering the rates at off-peak hours. This will help to shape up the demand curve of electricity by averaging out the usage over the time. Analysis of historical data will be used to estimate load shading requirement, power consumption, effective utilization, lowering the individual bill amount, Consumer level analysis and so on.Proposed methodology for Smart Electric Meter Application: 1. Build a Data warehouse: Schema, Tables (Referential Integrity)2. Generate Sample source files3. Perform validity check for data cleansing 4. ETL processing on raw data: Apply business rules on data5. Analyze the Data, Generate Bills 6. Conclusion
Other Details:
| Manuscript Id | : | IJSTEV1I10139
|
| Published in | : | Volume : 1, Issue : 10
|
| Publication Date | : | 01/05/2015
|
| Page(s) | : | 341-347
|
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