Air-conditioning load forecasting based on seasonal decomposition and ARIMA model

2021 ◽  
Author(s):  
Linyao Zhang ◽  
Pengjia Shi ◽  
Xinyi Lai ◽  
Peiren Du ◽  
Fushuan Wen ◽  
...  
Keyword(s):  
Air Conditioning ◽  
Arima Model ◽  
Load Forecasting ◽  
Seasonal Decomposition

A comparative study of short-term load forecasting methods in distribution network

2021 ◽  
Vol 15 (1) ◽  
pp. 23-35
Author(s):  
Tuan Ho Le ◽  
◽  
Quang Hung Le ◽  
Thanh Hoang Phan
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Arima Model ◽  
Distribution Network ◽  
Load Forecasting ◽  
Short Term ◽  
Load Demand ◽  
Forecasting Methods ◽  
Short Term Load Forecasting

Short-term load forecasting plays an important role in building operation strategies and ensuring reliability of any electric power system. Generally, short-term load forecasting methods can be classified into three main categories: statistical approaches, artificial intelligence based-approaches and hybrid approaches. Each method has its own advantages and shortcomings. Therefore, the primary objective of this paper is to investigate the effectiveness of ARIMA model (e.g., statistical method) and artificial neural network (e.g., artificial intelligence based-method) in short-term load forecasting of distribution network. Firstly, the short-term load demand of Quy Nhon distribution network and short-term load demand of Phu Cat distribution network are analyzed. Secondly, the ARIMA model is applied to predict the load demand of two distribution networks. Thirdly, the artificial neural network is utilized to estimate the load demand of these networks. Finally, the estimated results from two applied methods are conducted for comparative purposes.

Application Study of Load Forecasting of Central Air-Conditioning System Based on Time Sequence Analysis Method

2012 ◽  
Vol 220-223 ◽  
pp. 622-625
Author(s):  
Xue Li Zhu ◽  
Bo Dong ◽  
Yong Jun Zhu
Keyword(s):  
Sequence Analysis ◽  
Air Conditioning ◽  
Time Lag ◽  
Load Forecasting ◽  
Time Sequence ◽  
Analysis Method ◽  
Air Conditioning System ◽  
Central Air Conditioning ◽  
Heating Load ◽  
Time Sequence Analysis

With the characteristics of non-stationarity, non-linearity, time-lag of refrigeration/ heating supplying in minds, load forecasting of central air-conditioning system is carried using time sequence analysis method. Firstly, acquisition sample data of central air-conditioning system is pretreated, and random time sequence AR model of system is formulated. Then, forecasting of AR refrigeration/heating load based on Yule-walker method is conducted. In order to enhance forecasting accuracy, crossover forecasting is introduced into the load forecasting, that is, to use vertical forecasting to follow household demands for load and horizontal forecasting to track changes of weather. Then, weight cross is made to vertical and horizontal forecasting results. Finally, refrigeration/heating load forecasting software of central air-conditioning system is developed, which is used in energy-saving monitoring and control of central air-conditioning system.

Predicting the Air-Conditioning Load under Drought Conditions Based on ELM

2011 ◽  
Vol 474-476 ◽  
pp. 1326-1329
Author(s):  
Zhao Kun Wang ◽  
Xiao Yang Zhang ◽  
Ming Yong Lai ◽  
Bao Ping Liu
Keyword(s):  
Theoretical Basis ◽  
Air Conditioning ◽  
Meteorological Factors ◽  
Load Forecasting ◽  
Forecasting Model ◽  
Cooling Load ◽  
Power Sector ◽  
Feed Forward Neural Network ◽  
Drought Conditions ◽  
Hidden Layer

In this<b> </b>paper, a model based on ELM is proposed to predict the air-conditioning load under drought conditions by analyzing the daily average air- conditioning load during the drought. The main meteorological factors that impact the air-conditioning load are considered in the model, and then the air-conditioning load under drought conditions can be predicted by training the samples by the single hidden layer feed forward neural network of ELM. Thus, the model is used to provide good theoretical basis for management on the demand side of power sector. Finally, an example is showed to prove that the curve of the air-conditioning load forecasting model and the curve of the actual cooling load of the power are almost consistent, and the prediction is accurate, reliable, and can be applied to other load forecasting.

scholarly journals Air-Conditioning Load Forecasting for Prosumer Based on Meta Ensemble Learning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 123673-123682 ◽  
Author(s):  
Yaogang Chen ◽  
Guoyin Fu ◽  
Xuefeng Liu
Keyword(s):  
Ensemble Learning ◽  
Air Conditioning ◽  
Load Forecasting

The Forecasting Method Of Central Air Conditioning Load: A Brief Review

2021 ◽  
Vol 14 ◽  
Author(s):  
Mengxiang Zhuang ◽  
Qixin Zhu
Keyword(s):  
Air Conditioning ◽  
Load Forecasting ◽  
Current Status ◽  
Impact Factors ◽  
Future Research ◽  
Load Prediction ◽  
Advantages And Disadvantages ◽  
Operating Load ◽  
Forecasting Method ◽  
The Impact

Objective: In order to better understand the research results of AC load prediction and carry out new research, the Air Conditioning (AC) load forecasting method plays an important role in the energy consumption of AC. Method: This paper summarizes the methods of building AC load prediction, mainly from the impact factors of AC operating load and the methods of AC system operating load forecasting to introduce the current status of load prediction. This paper describes some studies on load influencing factors, compares the advantages and disadvantages of modeling methods for AC operation load prediction and points out the research direction of AC load forecasting. Results: The current research methods are summarized and analyzed. Traditional forecasting methods are no longer applicable to air conditioning systems. From the current research, combinatorial prediction has become a hot research object. This method combines two or more methods to reduce the prediction error and shorten the prediction time. Conclusion: This paper points out some shortcomings of the present research and the future research suggestions are given in the three aspects of sharing AC operation data, selecting the key factors of AC, and exploring the new methods.

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