Short-Term Load Forecasting Based on Big Data Technologies

2014 ◽  
Vol 687-691 ◽  
pp. 1186-1192 ◽  
Author(s):  
Xin Zhang ◽  
Ming Cheng ◽  
Yang Liu ◽  
Dong Hua Li ◽  
Rui Min Wu
Keyword(s):  
Big Data ◽  
Load Forecasting ◽  
Smart Meter ◽  
Short Term ◽  
Smart Meters ◽  
New Approach ◽  
Traditional System ◽  
Short Term Load Forecasting ◽  
Data Architecture ◽  
Big Data Technologies

In recent years, wide installation of smart meters and implementation of Smart Meter Management System (SMMS) provides data foundation for short-term load forecasting. In this paper, a new load forecasting approach is proposed based on big data technologies using smart meter data. The new approach analyzes the characteristics of numerous electricity users, which helps system operators identify influencing factors. Big data architecture can handle large amount of data and computation efforts. Compared with the traditional system load forecasting methods, this new approach produces better prediction accuracy.

scholarly journals Short-term load forecasting based on big data technologies

2015 ◽  
Vol 1 (3) ◽  
pp. 59-67 ◽  
Author(s):  
Pei Zhang ◽  
Xiaoyu Wu ◽  
Xiaojun Wang ◽  
Sheng Bi
Keyword(s):  
Big Data ◽  
Load Forecasting ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Big Data Technologies

Deep Ensemble Technique for Short-Term Load Forecasting Using Smart Meter Data

2021 ◽  
pp. 635-643
Author(s):  
A. L. Amutha ◽  
R. Annie Uthra ◽  
J. Preetha Roselyn ◽  
R. Golda Brunet
Keyword(s):  
Load Forecasting ◽  
Smart Meter ◽  
Short Term ◽  
Ensemble Technique ◽  
Short Term Load Forecasting

Design of a Fuzzy Logic Controller for Short-Term Load Forecasting With Randomly Varying Load

2021 ◽  
Vol 13 (4) ◽  
pp. 32-49
Author(s):  
D. V. N. Ananth ◽  
Lagudu Venkata Suresh Kumar ◽  
Tulasichandra Sekhar Gorripotu ◽  
Ahmad Taher Azar
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Load Forecasting ◽  
Short Term ◽  
Random Load ◽  
Traditional System ◽  
Energy Management Systems ◽  
Load Demand ◽  
Short Term Load Forecasting ◽  
Changes Over Time

Short-term load forecasting (STLF) is an integral component of energy management systems. In this paper, fuzzy logic-based algorithm is used for short-term load forecasting. The load changes over time and the goal is to satisfy the shift in demand and to maintain a fault as low as possible between the reference and real powers. The error in the load demand in mega-watt (MW) is compared with proposed technique as well as conventional methods. Three cases were investigated in which the load changes were 1) more random in nature, but the variance to the reference was more; 2) the random load changes were simpler, but a little different from the reference; and lastly, 3) the load changing was random, and the reference deviation was maximum. The results are analyzed for different load changes, and the corresponding results are verified using MATLAB. The deviation of the error value in load response is less experienced with a fuzzy logic controller than with a traditional system, and in fewer iterations, the objective function is also achieved.

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