Quantitative Analysis of the Relationship between Temperature and Power Load

2014 ◽  
Vol 986-987 ◽  
pp. 428-432 ◽  
Author(s):  
Jian Liang Zhong ◽  
Bei Zhao ◽  
Da Zhang ◽  
Hai Bao
Keyword(s):  
Load Forecasting ◽  
Electric Power System ◽  
Effect Of Temperature ◽  
Series Data ◽  
Practical Implementation ◽  
Regression Equations ◽  
Short Term ◽  
Power Load ◽  
Short Term Load Forecasting ◽  
The Relationship

This paper presents the results of a study regarding the relationship between temperature and power load of the electric power system. Weather-influenced load part is picked up from original load series data with the conclusion that the lagged effect of temperature on load is within 12 hours. Furthermore, decision tree and step regression methods are employed to get a group of decision trees and corresponding regression equations which are able to quantitatively describe the relationship between load and temperature. A short-term load forecasting algorithm is then developed and its practical implementation shows this quatitative analysis method could reliably reflect the influence of the temperature changes on the load and effectively improve the accuracy of short-term load forecasting.

Short-Term Load Forecasting for Electric Power System Based on DB Wavelet and Regression BP Neural Networks

2013 ◽  
Vol 380-384 ◽  
pp. 3018-3021
Author(s):  
Kun Zhang ◽  
Yan Hui Wang
Keyword(s):  
Neural Networks ◽  
Electric Power ◽  
Dynamic Balance ◽  
Load Forecasting ◽  
Electric Power System ◽  
Wavelet Coefficients ◽  
Bp Neural Networks ◽  
Short Term ◽  
Power Load ◽  
Short Term Load Forecasting

In order to ensure the dynamic balance of power load and improve the accuracy of short-term load forecasting, this paper presents a method of short-term load forecasting for electric power based on DB wavelet and regression BP neural networks. In this method, we get the wavelet coefficients at different scales through series decomposing of wavelet decomposition to load sample, and each scale wavelet coefficients for threshold selection, and then trained adjusted wavelet coefficients by regression BP neural networks, reconstructed load sequence predicted date through inverse wavelet transform. Finally, the accuracy of this method is significantly higher than BP neural network by examples verification.

Effect of temperature on short term load forecasting using an integrated ANN

2004 ◽  
Vol 72 (1) ◽  
pp. 95-101 ◽  
Author(s):  
B. Satish ◽  
K.S. Swarup ◽  
S. Srinivas ◽  
A.Hanumantha Rao
Keyword(s):  
Load Forecasting ◽  
Effect Of Temperature ◽  
Short Term ◽  
Short Term Load Forecasting

A NOVEL APPROACH FOR SHORT-TERM LOAD FORECASTING USING SUPPORT VECTOR MACHINES

2004 ◽  
Vol 14 (05) ◽  
pp. 329-335 ◽  
Author(s):  
LIANG TIAN ◽  
AFZEL NOORE
Keyword(s):  
Neural Network ◽  
Load Forecasting ◽  
Support Vector ◽  
Short Term ◽  
Feed Forward Neural Network ◽  
Modeling Approach ◽  
Novel Approach ◽  
Power Load ◽  
Short Term Load Forecasting ◽  
Network Approaches

A support vector machine (SVM) modeling approach for short-term load forecasting is proposed. The SVM learning scheme is applied to the power load data, forcing the network to learn the inherent internal temporal property of power load sequence. We also study the performance when other related input variables such as temperature and humidity are considered. The performance of our proposed SVM modeling approach has been tested and compared with feed-forward neural network and cosine radial basis function neural network approaches. Numerical results show that the SVM approach yields better generalization capability and lower prediction error compared to those neural network approaches.

scholarly journals Residential Short-Term Load Forecasting during Atypical Consumption Behavior

Energies ◽  
2022 ◽  
Vol 15 (1) ◽  
pp. 291
Author(s):  
Cristina Hora ◽  
Florin Ciprian Dan ◽  
Gabriel Bendea ◽  
Calin Secui
Keyword(s):  
Rural Areas ◽  
Moving Average ◽  
Load Forecasting ◽  
Short Term ◽  
Consumption Behavior ◽  
Best Response ◽  
Power Load ◽  
Short Term Load Forecasting ◽  
Residential Consumption ◽  
Forecasting Errors

Short-term load forecasting (STLF) is a fundamental tool for power networks’ proper functionality. As large consumers need to provide their own STLF, the residential consumers are the ones that need to be monitored and forecasted by the power network. There is a huge bibliography on all types of residential load forecast in which researchers have struggled to reach smaller forecasting errors. Regarding atypical consumption, we could see few titles before the coronavirus pandemic (COVID-19) restrictions, and afterwards all titles referred to the case of COVID-19. The purpose of this study was to identify, among the most used STLF methods—linear regression (LR), autoregressive integrated moving average (ARIMA) and artificial neural network (ANN)—the one that had the best response in atypical consumption behavior and to state the best action to be taken during atypical consumption behavior on the residential side. The original contribution of this paper regards the forecasting of loads that do not have reference historic data. As the most recent available scenario, we evaluated our forecast with respect to the database of consumption behavior altered by different COVID-19 pandemic restrictions and the cause and effect of the factors influencing residential consumption, both in urban and rural areas. To estimate and validate the results of the forecasts, multiyear hourly residential consumption databases were used. The main findings were related to the huge forecasting errors that were generated, three times higher, if the forecasting algorithm was not set up for atypical consumption. Among the forecasting algorithms deployed, the best results were generated by ANN, followed by ARIMA and LR. We concluded that the forecasting methods deployed retained their hierarchy and accuracy in forecasting error during atypical consumer behavior, similar to forecasting in normal conditions, if a trigger/alarm mechanism was in place and there was sufficient time to adapt/deploy the forecasting algorithm. All results are meant to be used as best practices during power load uncertainty and atypical consumption behavior.

A Short-Term Load Forecasting Method of Warship Based on PSO-SVM Method

2011 ◽  
Vol 127 ◽  
pp. 569-574
Author(s):  
Dong Liang Li ◽  
Xiao Feng Zhang ◽  
Ming Zhong Qiao ◽  
Gang Cheng
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Load Forecasting ◽  
Support Vector ◽  
Short Term ◽  
Swarm Optimization ◽  
Power Load ◽  
Forecasting Method ◽  
Short Term Load Forecasting ◽  
Adaptive Pso

The power load characteristics of warship on a specific task was analyzed,and a task-based forecasting method for warship short-term load forecasting was presented. the new influencing factors of warship power load were used in modeling which is different with the land grid and civilian vessels grid. Theory of particle swarm optimization and Support vector machine was disscused first, and the method of particle swarm optimization was improved to have the ability of adaptive parameter optimization. and the method of support vector machine was improved by the adaptive PSO optimizational method. then a new adaptive short-term load forecasting model was established by the adaptive PSO-SVM method. finally Through simulation results show that the adaptive PSO-SVM method is highly feasible to predict with high accuracy and high generalization capability.

scholarly journals Survey on short-term load forecasting using hybrid neural network techniques

2018 ◽  
Vol 7 (2.8) ◽  
pp. 464
Author(s):  
Shaive Dalela ◽  
Aditya Verma ◽  
A L.Amutha
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Load Forecasting ◽  
Theoretical Data ◽  
Electric Power System ◽  
Support Vector ◽  
Short Term ◽  
Load Demand ◽  
Short Term Load Forecasting ◽  
Artificial Neural

Load forecasting is an issue of great importance for the reliable operation of the electric power system grids. Various forecasting methodologies have been proposed in the international research bibliography, following different models and mathematical approaches. In the current work, several latest methodologies based on artificial neural networks along with other techniques have be discussed, in order to obtain short-term load forecasting. In this paper, approaches taken by different researchers considering different parameters in means of predicting the lease error has been shown.  The paper investigates the application of artificial neural networks (ANN) with fuzzy logic (FL), Genetic Algorithm(GA), Particle Swarm Optimization(PSO) and Support Vector Machines(SVM) as forecasting tools for predicting the load demand in short term category. The extracted outcomes indicate the effectiveness of the proposed method, reducing the relative error between real and theoretical data

Research for Short-Term Load Forecasting Based on Linearization Meteorological Factors

2014 ◽  
Vol 596 ◽  
pp. 700-703
Author(s):  
Shun Hua Zhang
Keyword(s):  
Factor Model ◽  
Meteorological Factors ◽  
Load Forecasting ◽  
Meteorological Factor ◽  
Short Term ◽  
Power Load ◽  
Short Term Load Forecasting ◽  
Electricity Load ◽  
Heating Load ◽  
The Impact

With the development of economy in recent years, rapid growth of electricity demand, the cooling and heating load gets more and more big proportion of the total electricity load; the power load is influenced by meteorological factors which become more and more big. This topic will be based on short-term load forecasting in ANN (Artificial Neural Networks), conduct further research on the relationship between meteorological factors and power load, find the impact of the core meteorological factors of power load, and linear core meteorological factor model to establish the suitable for load forecasting based on ANN, make the forecasting to correctly reflect the meteorological conditions, improve the prediction accuracy of short-term load forecasting.

scholarly journals Short-Term Load Forecasting Based on Deep Learning Bidirectional LSTM Neural Network

2021 ◽  
Vol 11 (17) ◽  
pp. 8129 ◽  
Author(s):  
Changchun Cai ◽  
Yuan Tao ◽  
Tianqi Zhu ◽  
Zhixiang Deng
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Power Systems ◽  
Network Architecture ◽  
Short Term Memory ◽  
Load Forecasting ◽  
Short Term ◽  
Architecture Model ◽  
Power Load ◽  
Short Term Load Forecasting

Accurate load forecasting guarantees the stable and economic operation of power systems. With the increasing integration of distributed generations and electrical vehicles, the variability and randomness characteristics of individual loads and the distributed generation has increased the complexity of power loads in power systems. Hence, accurate and robust load forecasting results are becoming increasingly important in modern power systems. The paper presents a multi-layer stacked bidirectional long short-term memory (LSTM)-based short-term load forecasting framework; the method includes neural network architecture, model training, and bootstrapping. In the proposed method, reverse computing is combined with forward computing, and a feedback calculation mechanism is designed to solve the coupling of before and after time-series information of the power load. In order to improve the convergence of the algorithm, deep learning training is introduced to mine the correlation between historical loads, and the multi-layer stacked style of the network is established to manage the power load information. Finally, actual data are applied to test the proposed method, and a comparison of the results of the proposed method with different methods shows that the proposed method can extract dynamic features from the data as well as make accurate predictions, and the availability of the proposed method is verified with real operational data.

scholarly journals Short-Term Power Load Forecasting Under COVID-19 Based on Graph Representation Learning With Heterogeneous Features

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhuowei Yu ◽  
Jiajun Yang ◽  
Yufeng Wu ◽  
Yi Huang
Keyword(s):  
Time Moment ◽  
Load Forecasting ◽  
Representation Learning ◽  
Graph Representation ◽  
Smooth Transition ◽  
Short Term ◽  
Power Load ◽  
Heterogeneous Features ◽  
Short Term Load Forecasting ◽  
Power Load Forecasting

Since 2020, the COVID-19 has spread globally at an extremely rapid rate. The epidemic, vaccination, and quarantine policies have profoundly changed economic development and human activities worldwide. As many countries start to resume economic activities aiming at a “living with COVID” new normal, a short-term load forecasting technique incorporating the epidemic’s effects is of great significance to both power system operation and a smooth transition. In this context, this paper proposes a novel short-term load forecasting method under COVID-19 based on graph representation learning with heterogeneous features. Unlike existing methods that fit power load data to time series, this study encodes heterogeneous features relevant to electricity consumption and epidemic status into a load graph so that not only the features at each time moment but also the inherent correlations between the features can be exploited; Then, a residual graph convolutional network (ResGCN) is constructed to fit the non-linear mappings from load graph to future loads. Besides, a graph concatenation method for parallel training is introduced to improve the learning efficiency. Using practical data in Houston, the annual, monthly, and daily effects of the crisis on power load are analyzed, which uncovers the strong correlation between the pandemic and the changes in regional electricity utilization. Moreover, the forecasting performance of the load graph-based ResGCN is validated by comparing with other representative methods. Its performance on MAPE and RMSE increased by 1.3264 and 15.03%, respectively. Codes related to all the simulations are available on https://github.com/YoungY6/ResGCN-for-Short-term-power-load-forecasting-under-COVID-19.

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