scholarly journals Optimal Decomposition and Reconstruction of Discrete Wavelet Transformation for Short-Term Load Forecasting

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4654 ◽  
Author(s):  
Happy Aprillia ◽  
Hong-Tzer Yang ◽  
Chao-Ming Huang
Keyword(s):  
Load Forecasting ◽  
Multiple Linear Regression Model ◽  
Discrete Wavelet ◽  
Short Term ◽  
Data Set ◽  
Prediction Ability ◽  
Special Events ◽  
Whale Optimization ◽  
Short Term Load Forecasting ◽  
Mlr Model

To achieve high accuracy in prediction, a load forecasting algorithm must model various consumer behaviors in response to weather conditions or special events. Different triggers will have various effects on different customers and lead to difficulties in constructing an adequate prediction model due to non-stationary and uncertain characteristics in load variations. This paper proposes an open-ended model of short-term load forecasting (STLF) which has general prediction ability to capture the non-linear relationship between the load demand and the exogenous inputs. The prediction method uses the whale optimization algorithm, discrete wavelet transform, and multiple linear regression model (WOA-DWT-MLR model) to predict both system load and aggregated load of power consumers. WOA is used to optimize the best combination of detail and approximation signals from DWT to construct an optimal MLR model. The proposed model is validated with both the system-side data set and the end-user data set for Independent System Operator-New England (ISO-NE) and smart meter load data, respectively, based on Mean Absolute Percentage Error (MAPE) criterion. The results demonstrate that the proposed method achieves lower prediction error than existing methods and can have consistent prediction of non-stationary load conditions that exist in both test systems. The proposed method is, thus, beneficial to use in the energy management system.

scholarly journals Short-term Load Forecasting for Microgrids Based on Discrete Wavelet Transform and BP Neural Network

2014 ◽  
Vol 8 (1) ◽  
pp. 738-742 ◽  
Author(s):  
Chong Gao ◽  
sheng Huang ◽  
Hai-feng Wang
Keyword(s):  
Wavelet Transform ◽  
Load Forecasting ◽  
Discrete Wavelet ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Micro Grid ◽  
Grid Load ◽  
The Time Domain ◽  
National Economies ◽  
Better Than

Electricity is of great vital and indispensable to national economies. A new short-term load forecasting for micro grid is proposed in this paper. After comparing and analyzing all load characteristic in the time domain and frequency domain, we apply wavelet transform to decompose the load signal. After that, the training set and text set are selected in consideration of the effects generated by the temperature and day type. At length, BP natural network is employed you forecast the micro grid load. The final result proves that the forecasting precision of the method we propose is obviously better than the traditional ones. What’s more, our method has Strong adaptability and good generalization ability.

scholarly journals Empirical Comparison of Neural Network and Auto-Regressive Models in Short-Term Load Forecasting

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2080 ◽  
Author(s):  
Miguel López ◽  
Carlos Sans ◽  
Sergio Valero ◽  
Carolina Senabre
Keyword(s):  
Load Forecasting ◽  
Ar Model ◽  
Empirical Comparison ◽  
Short Term ◽  
Data Set ◽  
Short Term Load Forecasting ◽  
System Operator ◽  
Auto Regressive ◽  
One Year ◽  
The Individual

Artificial Intelligence (AI) has been widely used in Short-Term Load Forecasting (STLF) in the last 20 years and it has partly displaced older time-series and statistical methods to a second row. However, the STLF problem is very particular and specific to each case and, while there are many papers about AI applications, there is little research determining which features of an STLF system is better suited for a specific data set. In many occasions both classical and modern methods coexist, providing combined forecasts that outperform the individual ones. This paper presents a thorough empirical comparison between Neural Networks (NN) and Autoregressive (AR) models as forecasting engines. The objective of this paper is to determine the circumstances under which each model shows a better performance. It analyzes one of the models currently in use at the National Transport System Operator in Spain, Red Eléctrica de España (REE), which combines both techniques. The parameters that are tested are the availability of historical data, the treatment of exogenous variables, the training frequency and the configuration of the model. The performance of each model is measured as RMSE over a one-year period and analyzed under several factors like special days or extreme temperatures. The AR model has 0.13% lower error than the NN under ideal conditions. However, the NN model performs more accurately under certain stress situations.

scholarly journals Energy Load Forecasting Using a Dual-Stage Attention-Based Recurrent Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7115
Author(s):  
Alper Ozcan ◽  
Cagatay Catal ◽  
Ahmet Kasif
Keyword(s):  
Time Series ◽  
Service Providers ◽  
Time Series Prediction ◽  
Load Forecasting ◽  
Short Term ◽  
Special Events ◽  
Proposed Model ◽  
Context Vector ◽  
Short Term Load Forecasting ◽  
Dual Stage

Providing a stable, low-price, and safe supply of energy to end-users is a challenging task. The energy service providers are affected by several events such as weather, volatility, and special events. As such, the prediction of these events and having a time window for taking preventive measures are crucial for service providers. Electrical load forecasting can be modeled as a time series prediction problem. One solution is to capture spatial correlations, spatial-temporal relations, and time-dependency of such temporal networks in the time series. Previously, different machine learning methods have been used for time series prediction tasks; however, there is still a need for new research to improve the performance of short-term load forecasting models. In this article, we propose a novel deep learning model to predict electric load consumption using Dual-Stage Attention-Based Recurrent Neural Networks in which the attention mechanism is used in both encoder and decoder stages. The encoder attention layer identifies important features from the input vector, whereas the decoder attention layer is used to overcome the limitations of using a fixed context vector and provides a much longer memory capacity. The proposed model improves the performance for short-term load forecasting (STLF) in terms of the Mean Absolute Error (MAE) and Root Mean Squared Errors (RMSE) scores. To evaluate the predictive performance of the proposed model, the UCI household electric power consumption (HEPC) dataset has been used during the experiments. Experimental results demonstrate that the proposed approach outperforms the previously adopted techniques.

scholarly journals Short-term load forecasting using Theta method

2019 ◽  
Vol 84 ◽  
pp. 01004 ◽  
Author(s):  
Grzegorz Dudek
Keyword(s):  
Empirical Studies ◽  
Load Forecasting ◽  
Seasonal Cycles ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Forecasting Methods ◽  
Short Term Load Forecasting ◽  
Data Definition ◽  
System Data ◽  
Theta Method

The Theta method attracted the attention of researchers and practitioners in recent years due to its simplicity and superior forecasting accuracy. Its performance has been confirmed by many empirical studies as well as forecasting competitions. In this article the Theta method is tested in short-term load forecasting problem. The load time series expressing multiple seasonal cycles is decomposed in different ways to simplify the forecasting problem. Four variants of input data definition are considered. The standard Theta method is uses as well as the dynamic optimised Theta model proposed recently. The performances of the Theta models are demonstrated through an empirical application using real power system data and compared with other popular forecasting methods.

Sign in / Sign up

Export Citation Format

Share Document