scholarly journals Short-Term Load Forecasting and Temperature Load Extraction Based on CEEMDAN and TDIC

CONVERTER ◽  
2021 ◽  
pp. 419-436
Author(s):  
Min Wang Et al.
Keyword(s):  
Dynamic Change ◽  
Load Forecasting ◽  
The United States ◽  
Ensemble Empirical Mode Decomposition ◽  
Percentage Error ◽  
Time Data ◽  
Short Term ◽  
Multi Scale ◽  
Temperature Load ◽  
Short Term Load Forecasting

With the intensification of urbanization in various countries worldwide, the temperature load which is greatly affected by ambient temperature, such as summer cooling loads and winter heating loads, accounts for a rising proportion of the total urban load. It causes an increasing peak-to-valley load difference. However, due to the complex composition and strong randomness of the load, it is necessary to study the multi-scale and multi-period correlation between temperature. Based on this, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to decompose the temperature and load into multi-scale components. The time-dependent intrinsic correlation (TDIC) is proposed to analyze the local correlation between temperature and load in multiple periods under a multi-scale framework, and obtain the dynamic change characteristics of the correlation between temperature and load. Based on the TDIC analysis results, a suitable sample period for short-term load forecasting (STLF) and input temperature data can be selected. Finally, extreme learning machine optimized by particle swarm optimization (PSO-ELM) is used to forecast each component of the load. The proposed STLF method is validated on real-time data from the Pennsylvania-New Jersey-Maryland (PJM) Company in the United States. The proposed method has greatly reduced in both mean absolute percentage error (MAPE) and root mean square error (RMSE) compared with other traditional methods, and the temperature load that fluctuates with temperature in the day to be forecasted is extracted.

scholarly journals A Hybrid Short-Term Load Forecasting Framework with an Attention-Based Encoder–Decoder Network Based on Seasonal and Trend Adjustment

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4612 ◽  
Author(s):  
Zhaorui Meng ◽  
Xianze Xu
Keyword(s):  
Power System ◽  
Load Forecasting ◽  
The United States ◽  
Attention Mechanism ◽  
Gradient Boosting ◽  
Percentage Error ◽  
Electrical Load ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Gated Recurrent Units

Accurate electrical load forecasting plays an important role in power system operation. An effective load forecasting approach can improve the operation efficiency of a power system. This paper proposes the seasonal and trend adjustment attention encoder–decoder (STA–AED), a hybrid short-term load forecasting approach based on a multi-head attention encoder–decoder module with seasonal and trend adjustment. A seasonal and trend decomposing technique is used to preprocess the original electrical load data. Each decomposed datum is regressed to predict the future electric load value by utilizing the encoder–decoder network with the multi-head attention mechanism. With the multi-head attention mechanism, STA–AED can interpret the prediction results more effectively. A large number of experiments and extensive comparisons have been carried out with a load forecasting dataset from the United States. The proposed hybrid STA–AED model is superior to the other five counterpart models such as random forest, gradient boosting decision tree (GBDT), gated recurrent units (GRUs), Encoder–Decoder, and Encoder–Decoder with multi-head attention. The proposed hybrid model shows the best prediction accuracy in 14 out of 15 zones in terms of both root mean square error (RMSE) and mean absolute percentage error (MAPE).

scholarly journals Multi-Scale Convolutional Neural Network With Time-Cognition for Multi-Step Short-Term Load Forecasting

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 88058-88071 ◽  
Author(s):  
Zhuofu Deng ◽  
Binbin Wang ◽  
Yanlu Xu ◽  
Tengteng Xu ◽  
Chenxu Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Load Forecasting ◽  
Short Term ◽  
Multi Scale ◽  
Short Term Load Forecasting

scholarly journals A Short-Term Load Forecasting Method Based on GRU-CNN Hybrid Neural Network Model

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Lizhen Wu ◽  
Chun Kong ◽  
Xiaohong Hao ◽  
Wei Chen
Keyword(s):  
Neural Network ◽  
Power System ◽  
Network Model ◽  
Neural Network Model ◽  
Feature Vector ◽  
Load Forecasting ◽  
Percentage Error ◽  
Short Term ◽  
Hybrid Neural Network ◽  
Short Term Load Forecasting

Short-term load forecasting (STLF) plays a very important role in improving the economy and stability of the power system operation. With the smart meters and smart sensors widely deployed in the power system, a large amount of data was generated but not fully utilized, these data are complex and diverse, and most of the STLF methods cannot well handle such a huge, complex, and diverse data. For better accuracy of STLF, a GRU-CNN hybrid neural network model which combines the gated recurrent unit (GRU) and convolutional neural networks (CNN) was proposed; the feature vector of time sequence data is extracted by the GRU module, and the feature vector of other high-dimensional data is extracted by the CNN module. The proposed model was tested in a real-world experiment, and the mean absolute percentage error (MAPE) and the root mean square error (RMSE) of the GRU-CNN model are the lowest among BPNN, GRU, and CNN forecasting methods; the proposed GRU-CNN model can more fully use data and achieve more accurate short-term load forecasting.

scholarly journals Short-Term Load Forecasting Based on the Transformer Model

Information ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 516
Author(s):  
Zezheng Zhao ◽  
Chunqiu Xia ◽  
Lian Chi ◽  
Xiaomin Chang ◽  
Wei Li ◽  
...  
Keyword(s):  
Load Forecasting ◽  
Percentage Error ◽  
Short Term ◽  
Proposed Model ◽  
Consumption Data ◽  
Short Term Load Forecasting ◽  
Distribution Process ◽  
Set Up ◽  
Transformer Model ◽  
Similar Day

From the perspective of energy providers, accurate short-term load forecasting plays a significant role in the energy generation plan, efficient energy distribution process and electricity price strategy optimisation. However, it is hard to achieve a satisfactory result because the historical data is irregular, non-smooth, non-linear and noisy. To handle these challenges, in this work, we introduce a novel model based on the Transformer network to provide an accurate day-ahead load forecasting service. Our model contains a similar day selection approach involving the LightGBM and k-means algorithms. Compared to the traditional RNN-based model, our proposed model can avoid falling into the local minimum and outperforming the global search. To evaluate the performance of our proposed model, we set up a series of simulation experiments based on the energy consumption data in Australia. The performance of our model has an average MAPE (mean absolute percentage error) of 1.13, where RNN is 4.18, and LSTM is 1.93.

scholarly journals Performance Evaluation of Different Membership Function in Fuzzy Logic Based Short-Term Load Forecasting

2021 ◽  
Vol 29 (2) ◽  
Author(s):  
Oladimeji Ibrahim ◽  
Waheed Olaide Owonikoko ◽  
Abubakar Abdulkarim ◽  
Abdulrahman Okino Otuoze ◽  
Mubarak Akorede Afolayan ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Membership Function ◽  
Load Forecasting ◽  
Percentage Error ◽  
Short Term ◽  
Load Forecast ◽  
Utility Company ◽  
Load Demand ◽  
Short Term Load Forecasting ◽  
Fuzzy Logic Technique

A mismatch between utility-scale electricity generation and demand often results in resources and energy wastage that needed to be minimized. Therefore, the utility company needs to be able to accurately forecast load demand as a guide for the planned generation. Short-term load forecast assists the utility company in projecting the future energy demand. The predicted load demand is used to plan ahead for the power to be generated, transmitted, and distributed and which is crucial to power system reliability and economics. Recently, various methods from statistical, artificial intelligence, and hybrid methods have been widely used for load forecasts with each having their merits and drawbacks. This paper investigates the application of the fuzzy logic technique for short-term load forecast of a day ahead load. The developed fuzzy logic model used time, temperature, and historical load data to forecast 24 hours load demand. The fuzzy models were based on both the trapezoidal and triangular membership function (MF) to investigate their accuracy and effectiveness for the load forecast. The obtained low Mean Absolute Percentage Error (MAPE), Mean Forecast Error (MFE), and Mean Absolute Deviation (MAD) values from the forecasted load results showed that both models are suitable for short-term load forecasting, however the trapezoidal MF showed better performance than the triangular MF.

scholarly journals Layer Recurrent Neural Network based Power System Load Forecasting

2015 ◽  
Vol 16 (3) ◽  
pp. 423
Author(s):  
Nikita Mittal ◽  
Akash Saxena
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Load Forecasting ◽  
Absolute Error ◽  
Percentage Error ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Load Pattern ◽  
Facility Expansion ◽  
Large Distribution

This paper presents a straight forward application of Layer Recurrent Neural Network (LRNN) to predict the load of a large distribution network. Short term load forecasting provides important information about the system’s load pattern, which is a premier requirement in planning periodical operations and facility expansion. Approximation of data patterns for forecasting is not an easy task to perform. In past, various approaches have been applied for forecasting. In this work application of LRNN is explored. The results of proposed architecture are compared with other conventional topologies of neural networks on the basis of Root Mean Square of Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE). It is observed that the results obtained from LRNN are comparatively more significant.

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 Deep Learning-Based Short-Term Load Forecasting for Supporting Demand Response Program in Hybrid Energy System

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3359 ◽  
Author(s):  
Sholeh Hadi Pramono ◽  
Mahdin Rohmatillah ◽  
Eka Maulana ◽  
Rini Nur Hasanah ◽  
Fakhriy Hario
Keyword(s):  
Deep Learning ◽  
Demand Response ◽  
Load Forecasting ◽  
Percentage Error ◽  
Short Term ◽  
Validation Data ◽  
Hybrid Energy ◽  
Testing Dataset ◽  
Short Term Load Forecasting ◽  
Demand Response Program

A novel method for short-term load forecasting (STLF) is proposed in this paper. The method utilizes both long and short data sequences which are fed to a wavenet based model that employs dilated causal residual convolutional neural network (CNN) and long short-term memory (LSTM) layer respectively to hourly forecast future load demand. This model is aimed to support the demand response program in hybrid energy systems, especially systems using renewable and fossil sources. In order to prove the generality of our model, two different datasets are used which are the ENTSO-E (European Network of Transmission System Operators for Electricity) dataset and ISO-NE (Independent System Operator New England) dataset. Moreover, two different ways of model testing are conducted. The first is testing with the dataset having identical distribution with validation data, while the second is testing with data having unknown distribution. The result shows that our proposed model outperforms other deep learning-based model in terms of root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). In detail, our model achieves RMSE, MAE, and MAPE equal to 203.23, 142.23, and 2.02 for the ENTSO-E testing dataset 1 and 292.07, 196.95 and 3.1 for ENTSO-E dataset 2. Meanwhile, in the ISO-NE dataset, the RMSE, MAE, and MAPE equal to 85.12, 58.96, and 0.4 for ISO-NE testing dataset 1 and 85.31, 62.23, and 0.46 for ISO-NE dataset 2.

Sign in / Sign up

Export Citation Format

Share Document