Identification of ARMAX model for short term load forecasting: an evolutionary programming approach

1996 ◽  
Vol 11 (1) ◽  
pp. 403-408 ◽  
Author(s):  
Hong-Tzer Yang ◽  
Chao-Ming Huang ◽  
Ching-Lien Huang
Keyword(s):  
Load Forecasting ◽  
Evolutionary Programming ◽  
Programming Approach ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Armax Model

scholarly journals Optimized Operation of Integrated Energy Microgrid with Energy Storage Based on Short-Term Load Forecasting

Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 22
Author(s):  
Hanlin Dong ◽  
Zhijian Fang ◽  
Al-wesabi Ibrahim ◽  
Jie Cai
Keyword(s):  
Energy Storage ◽  
Optimization Technique ◽  
Load Forecasting ◽  
Energy System ◽  
Mixed Integer ◽  
Programming Approach ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Integrated Energy System ◽  
Optimal Coordination

This research proposes an optimization technique for an integrated energy system that includes an accurate prediction model and various energy storage forms to increase load forecast accuracy and coordinated control of various energies in the current integrated energy system. An artificial neural network is utilized to create an accurate short-term load forecasting model to effectively predict user demand. The 0–1 mixed integer linear programming approach is used to analyze the optimal control strategy for multiple energy systems with storage, cold energy, heat energy, and electricity to solve the problem of optimal coordination. Simultaneously, a precise load forecasting method and an optimal scheduling strategy for multienergy systems are proposed. The equipment scheduling plan of the integrated energy system of gas, heat, cold, and electricity is proposed after researching the operation characteristics and energy use process of the equipment in the combined power supply system. A system economic operation model is created with profit maximization in mind, while also taking into account energy coordination between energy and the power grid. The rationality of the algorithm and model is verified by analyzing the real data of a distributed energy station in Wuhan for two years.

scholarly journals Short-term load forecasting using Theta method

2019 ◽  
Vol 84 ◽  
pp. 01004 ◽  
Author(s):  
Grzegorz Dudek
Keyword(s):  
Empirical Studies ◽  
Load Forecasting ◽  
Seasonal Cycles ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Forecasting Methods ◽  
Short Term Load Forecasting ◽  
Data Definition ◽  
System Data ◽  
Theta Method

The Theta method attracted the attention of researchers and practitioners in recent years due to its simplicity and superior forecasting accuracy. Its performance has been confirmed by many empirical studies as well as forecasting competitions. In this article the Theta method is tested in short-term load forecasting problem. The load time series expressing multiple seasonal cycles is decomposed in different ways to simplify the forecasting problem. Four variants of input data definition are considered. The standard Theta method is uses as well as the dynamic optimised Theta model proposed recently. The performances of the Theta models are demonstrated through an empirical application using real power system data and compared with other popular forecasting methods.

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