scholarly journals A Load Forecasting Analysis Method Considering Multiple Demand Side Resources

2015 ◽  
Author(s):  
Long Zhou ◽  
Dunnan Liu ◽  
Zhanhui Xiao ◽  
Qiang Wang
Keyword(s):  
Load Forecasting ◽  
Demand Side ◽  
Analysis Method ◽  
Forecasting Analysis

scholarly journals Optimal Scheme with Load Forecasting for Demand Side Management (DSM) in Residential Areas

2013 ◽  
Vol 05 (04) ◽  
pp. 889-896 ◽  
Author(s):  
Mohamed AboGaleela ◽  
Magdy El-Marsafawy ◽  
Mohamed El-Sobki
Keyword(s):  
Load Forecasting ◽  
Demand Side Management ◽  
Demand Side ◽  
Residential Areas ◽  
Optimal Scheme

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.

Multi-Stage Distribution Network Space Load Forecasting Method Considering Demand Side Resources

2020 ◽  
Vol 15 (12) ◽  
pp. 1474-1481
Author(s):  
Zhidong Yang ◽  
Guangjiu Chen ◽  
Jianwu Ding ◽  
Xiaojing Kang ◽  
Meng Sheng
Keyword(s):  
Distribution Network ◽  
Load Forecasting ◽  
Processing System ◽  
Prediction Method ◽  
Operation Time ◽  
Specific Information ◽  
Demand Side ◽  
Multi Stage ◽  
Prediction Function ◽  
Forecasting Method

Under the background of the further development of electric power, this paper forecasts the spatial load of distribution network, and proposes a multi-stage spatial load forecasting method considering the demand side resources. Firstly, the load of distribution network is pretreated to improve the prediction function of the processing system, and the working efficiency of the whole system is enhanced to solve the maximum load value. Then, the different conditions of demand side resources are considered step by step to realize the fine analysis, confirm the saturation density value of load, understand the specific information of spatial load, master the predicted data status, and finally carry out the comprehensive prediction method research of spatial load to realize the prediction research of spatial load of distribution network. The experimental results show that the multi-stage spatial load forecasting method considering demand side resources has high accuracy and reliability, and its forecasting effect can improve the system forecasting performance to a certain extent, reduce unnecessary operation time, reduce energy and resource consumption, and promote the development of load forecasting research.

scholarly journals Short-term Load Forecasting Model Considering Meteorological Factors

2019 ◽  
Vol 118 ◽  
pp. 02050
Author(s):  
Xi Yunhua ◽  
Zhu Haojun ◽  
Dong Nan
Keyword(s):  
Decision Tree ◽  
Meteorological Factors ◽  
Load Forecasting ◽  
Forecasting Model ◽  
Analysis Method ◽  
Decision Tree Algorithm ◽  
Characteristic Analysis ◽  
Short Term ◽  
Tree Algorithm ◽  
Short Term Load Forecasting

Because of the limitation of basic data and processing methods, the traditional load characteristic analysis method can not achieve user-level refined prediction. This paper builds a user-level short-term load forecasting model based on algorithms such as decision trees and neural networks in big data technology. Firstly, based on the grey relational analysis method, the influence of meteorological factors on load characteristics is quantitatively analyzed. The key factors are selected as input vectors of decision tree algorithm. This paper builds a category label for each daily load curve after clustering the user’s historical load data. The decision tree algorithm is used to establish classification rules and classify the days to be predicted. Finally, Elman neural network is used to predict the short-term load of a user, and the validity of the model is verified.

scholarly journals Load Forecasting Analysis by Time Series Method

2018 ◽  
Vol 7 (2.4) ◽  
pp. 101
Author(s):  
Kuldeep S ◽  
Anitha G S
Keyword(s):  
Time Series ◽  
Power Systems ◽  
Load Forecasting ◽  
Economic Operation ◽  
Load Pattern ◽  
Time Series Method ◽  
Forecasting Analysis ◽  
And Behavior ◽  
Load Behavior ◽  
Tools And Methods

Load forecasting is a very important factor for designing power systems. A good knowledge of load pattern and behavior is very important for proper coordination, design and economic operation. Though a lot of research has been done on load forecasting, there are many tools and methods still being developed to accurately predict load behavior. This paper does an analysis of sample load data and predicts the next instant load using feedforward time series neural network model

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