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 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.

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.

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 Online SARIMA applied for short-term electricity load forecasting

2021 ◽  
Author(s):  
Quang Dat Nguyen ◽  
Nhat Anh Nguyen ◽  
Ngoc Thang Tran ◽  
Vijender Kumar Solanki ◽  
Rubén González Crespo ◽  
...  
Keyword(s):  
Power Systems ◽  
Moving Average ◽  
Supply And Demand ◽  
Load Forecasting ◽  
Percentage Error ◽  
Short Term ◽  
Autoregressive Integrated Moving Average ◽  
Proposed Model ◽  
Short Term Load Forecasting ◽  
Electricity Load

Abstract Short-term Load Forecasting (STLF) plays a crucial role in balancing supply and demand of load dispatching operation, ensures stability for the power system. With the advancement of real-time smart sensors in power systems, it is of great significance to develop techniques to handle data streams on-the-fly to improve operational efficiency. In this paper, we propose an online variant of Seasonal Autoregressive Integrated Moving Average (SARIMA) to forecast electricity load sequentially. The proposed model is utilized to forecast hourly electricity load of northern Vietnam and achieves a mean absolute percentage error (MAPE) of 4.57%.

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.

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.

scholarly journals A Hybrid Approach to Short-Term Load Forecasting Aimed at Bad Data Detection in Secondary Substation Monitoring Equipment

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3947 ◽  
Author(s):  
Pedro Martín ◽  
Guillermo Moreno ◽  
Francisco Javier Rodríguez ◽  
José Antonio Jiménez ◽  
Ignacio Fernández
Keyword(s):  
State Estimation ◽  
Measurement Errors ◽  
Load Forecasting ◽  
The State ◽  
Short Term ◽  
Monitoring Equipment ◽  
Hybrid Strategy ◽  
Power Load ◽  
Short Term Load Forecasting ◽  
Bad Data

Bad data as a result of measurement errors in secondary substation (SS) monitoring equipment is difficult to detect and negatively affects power system state estimation performance by both increasing the computational burden and jeopardizing the state estimation accuracy. In this paper a short-term load forecasting (STLF) hybrid strategy based on singular spectrum analysis (SSA) in combination with artificial neural networks (ANN), is presented. This STLF approach is aimed at detecting, identifying and eliminating and/or correcting such bad data before it is provided to the state estimator. This approach is developed to improve the accuracy of the load forecasts and it is tested against real power load data provided by electricity suppliers. Depending on the week considered, mean absolute percentage error (MAPE) values which range from 1.6% to 3.4% are achieved for STLF. Different systematic errors, such as gain and offset error levels and outliers, are successfully detected with a hit rate of 98%, and the corresponding measurements are corrected before they are sent to the control center for state estimation purposes.

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