Short-term load forecasting in electrical power systems via trajectory tracking and error correcting approach

2014 ◽  
Vol 6 (1) ◽  
pp. 013112 ◽  
Author(s):  
Yongduan Song ◽  
Zhixi Shen ◽  
Donglin Dai ◽  
Yanan Qian ◽  
Yujuan Wang
Keyword(s):  
Power Systems ◽  
Trajectory Tracking ◽  
Electrical Power ◽  
Load Forecasting ◽  
Short Term ◽  
Electrical Power Systems ◽  
Short Term Load Forecasting

Mathematical Programming Based Short Term Load Forecasting Algorithm, Case Study: Turkey 2010

2012 ◽  
Vol 433-440 ◽  
pp. 3934-3938
Author(s):  
Nurettin Çetinkaya
Keyword(s):  
Mathematical Programming ◽  
Power Systems ◽  
Computational Algorithm ◽  
Electrical Power ◽  
Load Forecasting ◽  
Short Term ◽  
Electrical Power Systems ◽  
Short Term Load Forecasting ◽  
The Mean

Short-term load forecasting (STLF) is an important problem in the operation of electrical power generation and transmission. In this paper, STLF algorithm was developed for electrical power systems using mathematical programming with Matlab. A fast and efficient computational algorithm has been obtained for STLF. The mean absolute percentage errors (MAPE) of daily loads forecast and weekly loads forecast for Turkey are found as 1,76%, 1,92%, respectively.

ANN-Based Short-Term Load Forecasting

2018 ◽  
pp. 92-111
Keyword(s):  
Power Systems ◽  
Transmission Lines ◽  
Prediction Models ◽  
Electrical Power ◽  
Load Forecasting ◽  
Back Propagation ◽  
Electrical Energy ◽  
Load Prediction ◽  
Electrical Power Systems ◽  
Short Term Load Forecasting

Load forecasting is a very crucial issue for the operational planning of electrical power systems. In the sixth chapter, it is formulated that a reliable power network along with load prediction models is essential for uninterrupted supply of electrical energy to the consumers. The Back-Propagation ANN algorithm is applied to forecast the load of the power system. Based on the load forecasted power components, transmission lines and sub-stations are augmented for improved reliability in a province.

scholarly journals Short-Term Load Forecasting for Electric Vehicle Charging Stations Based on Deep Learning Approaches

2019 ◽  
Vol 9 (9) ◽  
pp. 1723 ◽  
Author(s):  
Juncheng Zhu ◽  
Zhile Yang ◽  
Yuanjun Guo ◽  
Jiankang Zhang ◽  
Huikun Yang
Keyword(s):  
Deep Learning ◽  
Power Systems ◽  
Unit Commitment ◽  
Combustion Engine ◽  
Load Forecasting ◽  
Learning Approaches ◽  
Short Term ◽  
Load Curve ◽  
Short Term Load Forecasting ◽  
Gated Recurrent Units

Short-term load forecasting is a key task to maintain the stable and effective operation of power systems, providing reasonable future load curve feeding to the unit commitment and economic load dispatch. In recent years, the boost of internal combustion engine (ICE) based vehicles leads to the fossil fuel shortage and environmental pollution, bringing significant contributions to the greenhouse gas emissions. One of the effective ways to solve problems is to use electric vehicles (EVs) to replace the ICE based vehicles. However, the mass rollout of EVs may cause severe problems to the power system due to the huge charging power and stochastic charging behaviors of the EVs drivers. The accurate model of EV charging load forecasting is, therefore, an emerging topic. In this paper, four featured deep learning approaches are employed and compared in forecasting the EVs charging load from the charging station perspective. Numerical results show that the gated recurrent units (GRU) model obtains the best performance on the hourly based historical data charging scenarios, and it, therefore, provides a useful tool of higher accuracy in terms of the hourly based short-term EVs load forecasting.

scholarly journals ANN-Based Day-Ahead Short Term Load Forecasting

2021 ◽  
Author(s):  
Heng Zhao
Keyword(s):  
Power Systems ◽  
Power System ◽  
Meteorological Data ◽  
Load Forecasting ◽  
Automatic Generation ◽  
Training Algorithms ◽  
Security Assessment ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
And Performance

Load forecasting (LF) is of great significance for effective operation, utilization, safety and reliability of the modern electric power systems. Load forecasting can be categorized into very short term, short-term, medium-term, and long-term forecasts, depending on which time scale is concerned. The short term load forecasting (STLF) plays an increasingly important role in achieving a more efficient, reliable and safe power system. Its outputs are the indispensable inputs of generating scheduling, power system security assessment and power dispatch. In the era of smart grid (SG), STLF is the basic building block to imply Demand Side Management (DSM) in areas such as automatic generation control, load estimation, energy purchasing, and contract evaluation, etc. The accuracy of STLF is of essential importance for both economic and reliability. In the last few decades, various methods have been devised and applied to perform STLF. Due to its superior capability of handling the nonlinearity, Artificial Intelligence (AI) based techniques are gaining more popularity in a variety of applications. The objective of this study is to review, categorize, evaluate, and analyze the principle, application, and performance of STLF techniques. It builds up several feed forward Artificial Neural Networks (ANN) models with different configurations, and studies the mechanism of ANN for effective STLF. With 12 years of hourly load and meteorological data sets of a section of the City of Toronto, the configurations are built up with different hidden layers, activating function, training algorithms and both un-normalized and normalized data to predict the day ahead STLF with satisfactory result achieved.

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