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 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 Research on Regional Short-Term Power Load Forecasting Model and Case Analysis

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1617
Author(s):  
Kang Qian ◽  
Xinyi Wang ◽  
Yue Yuan
Keyword(s):  
Meteorological Factors ◽  
Load Forecasting ◽  
Weighted Average ◽  
Support Vector ◽  
Forecasting Model ◽  
Electric Load ◽  
Short Term ◽  
Power Load ◽  
Power Load Forecasting ◽  
Electric Load Forecasting

Integrated energy services will have multiple values and far-reaching significance in promoting energy transformation and serving “carbon peak and carbon neutralization”. In order to balance the supply and demand of power system in integrated energy, it is necessary to establish a scientific model for power load forecasting. Different algorithms for short-term electric load forecasting considering meteorological factors are presented in this paper. The correlation between electric load and meteorological factors is first analyzed. After the principal component analysis (PCA) of meteorological factors and autocorrelation analysis of the electric load, the daily load forecasting model is established by optimal support vector machine (OPT-SVM), Elman neural network (ENN), as well as their combinations through linear weighted average, geometric weighted average, and harmonic weighted average method, respectively. Based on the actual data of an industrial park of Nantong in China, the prediction performance in the four seasons with the different models is evaluated. The main contribution of this paper is to compare the effectiveness of different models for short-term electric load forecasting and to give a guideline to build the proper methods for load forecasting.

The study of the dynamic load forecasting model about air-conditioning system based on the terminal user load

2015 ◽  
Vol 94 ◽  
pp. 263-268 ◽  
Author(s):  
Xiaoning Xu ◽  
Gongsheng Huang ◽  
Hanwei Liu ◽  
Liuzhi Chen ◽  
Qingjun Liu
Keyword(s):  
Dynamic Load ◽  
Air Conditioning ◽  
Load Forecasting ◽  
Forecasting Model ◽  
Air Conditioning System

scholarly journals A Combined Model Based on EOBL-CSSA-LSSVM for Power Load Forecasting

Symmetry ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1579
Author(s):  
Xinheng Wang ◽  
Xiaojin Gao ◽  
Zuoxun Wang ◽  
Chunrui Ma ◽  
Zengxu Song
Keyword(s):  
Prediction Model ◽  
Spectrum Analysis ◽  
Search Algorithm ◽  
Singular Spectrum Analysis ◽  
Load Forecasting ◽  
Forecasting Model ◽  
Power Sector ◽  
Power Load ◽  
Power Load Forecasting ◽  
Electricity Load

Inaccurate electricity load forecasting can lead to the power sector gaining asymmetric information in the supply and demand relationship. This asymmetric information can lead to incorrect production or generation plans for the power sector. In order to improve the accuracy of load forecasting, a combined power load forecasting model based on machine learning algorithms, swarm intelligence optimization algorithms, and data pre-processing is proposed. Firstly, the original signal is pre-processed by the VMD–singular spectrum analysis data pre-processing method. Secondly, the noise-reduced signals are predicted using the Elman prediction model optimized by the sparrow search algorithm, the ELM prediction model optimized by the chaotic adaptive whale algorithm (CAWOA-ELM), and the LSSVM prediction model optimized by the chaotic sparrow search algorithm based on elite opposition-based learning (EOBL-CSSA-LSSVM) for electricity load data, respectively. Finally, the weighting coefficients of the three prediction models are calculated using the simulated annealing algorithm and weighted to obtain the prediction results. Comparative simulation experiments show that the VMD–singular spectrum analysis method and two improved intelligent optimization algorithms proposed in this paper can effectively improve the prediction accuracy. Additionally, the combined forecasting model proposed in this paper has extremely high forecasting accuracy, which can help the power sector to develop a reasonable production plan and power generation plans.

scholarly journals A Weekend Load Forecasting Model Based on Semi-Parametric Regression Analysis Considering Weather and Load Interaction

Energies ◽  
2019 ◽  
Vol 12 (20) ◽  
pp. 3820 ◽  
Author(s):  
Bin Li ◽  
Mingzhen Lu ◽  
Yiyi Zhang ◽  
Jia Huang
Keyword(s):  
Meteorological Factors ◽  
Normal Load ◽  
Load Forecasting ◽  
Maximum Load ◽  
Effect Of Temperature ◽  
Forecasting Model ◽  
Daily Maximum ◽  
Short Term ◽  
Parametric Regression ◽  
Load Characteristics

Compared to the load characteristics of normal working days, weekend load characteristics have a low level of load and are sensitive to meteorological conditions, which influences the accuracy of short-term weekend-load forecasting. To solve this problem and to improve the accuracy of short-term weekend-load forecasting, a Semi-parametric weekend-load forecasting method based on the interaction between meteorological and load is proposed in this paper. The main work is shown as follows: (1) through separating weekend-load from normal-load and analyzing the correlation between meteorological factors and daily maximum load, the meteorological factors with parameter characteristics and non-parameter characteristics can be screened out; (2) a short-term weekend-load forecasting model is built according to Semi-parametric regression theory which can express the coupling relation between meteorology and load more realistically; (3) the effect of temperature accumulation is also considered to correct the forecasting model. The proposed method is proved by implementing short-term weekend-load forecasting on the real historical data of the Southern Power Grid in China. The result shows that the 96-point mean load forecasting accuracy obtained by this model can meet the requirement of power network operation.

scholarly journals Load forecasting of refrigerated display cabinet based on CEEMD–IPSO–LSTM combined model

Open Physics ◽  
2021 ◽  
Vol 19 (1) ◽  
pp. 360-374
Author(s):  
Yuan Pei ◽  
Lei Zhenglin ◽  
Zeng Qinghui ◽  
Wu Yixiao ◽  
Lu Yanli ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
Load Forecasting ◽  
Series Data ◽  
Forecasting Model ◽  
Combined Model ◽  
Forecasting Accuracy ◽  
Forecasting Method ◽  
Consumption Reduction

Abstract The load of the showcase is a nonlinear and unstable time series data, and the traditional forecasting method is not applicable. Deep learning algorithms are introduced to predict the load of the showcase. Based on the CEEMD–IPSO–LSTM combination algorithm, this paper builds a refrigerated display cabinet load forecasting model. Compared with the forecast results of other models, it finally proves that the CEEMD–IPSO–LSTM model has the highest load forecasting accuracy, and the model’s determination coefficient is 0.9105, which is obviously excellent. Compared with other models, the model constructed in this paper can predict the load of showcases, which can provide a reference for energy saving and consumption reduction of display cabinet.

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