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 Use of Available Daylight to Improve Short-Term Load Forecasting Accuracy

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 95
Author(s):  
Miguel López ◽  
Sergio Valero ◽  
Carlos Sans ◽  
Carolina Senabre
Keyword(s):  
Load Forecasting ◽  
Linearization Method ◽  
Ar Model ◽  
Forecasting Model ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Electric System ◽  
Forecasting Error ◽  
Short Term Load Forecasting ◽  
System Operator

This paper introduces a new methodology to include daylight information in short-term load forecasting (STLF) models. The relation between daylight and power consumption is obvious due to the use of electricity in lighting in general. Nevertheless, very few STLF systems include this variable as an input. In addition, an analysis of one of the current STLF models at the Spanish Transmission System Operator (TSO), shows two humps in its error profile, occurring at sunrise and sunset times. The new methodology includes properly treated daylight information in STLF models in order to reduce the forecasting error during sunrise and sunset, especially when daylight savings time (DST) one-hour time shifts occur. This paper describes the raw information and the linearization method needed. The forecasting model used as the benchmark is currently used at the TSO’s headquarters and it uses both autoregressive (AR) and neural network (NN) components. The method has been designed with data from the Spanish electric system from 2011 to 2017 and tested over 2018 data. The results include a justification to use the proposed linearization over other techniques as well as a thorough analysis of the forecast results yielding an error reduction in sunset hours from 1.56% to 1.38% for the AR model and from 1.37% to 1.30% for the combined forecast. In addition, during the weeks in which DST shifts are implemented, sunset error drops from 2.53% to 2.09%.

scholarly journals ANN based Short-Term Load Curve Forecasting

2018 ◽  
Vol 13 (6) ◽  
pp. 938-955
Author(s):  
Violeta Eugenia Chis ◽  
Constantin Barbulescu ◽  
Stefan Kilyeni ◽  
Simona Dzitac
Keyword(s):  
Distribution System ◽  
Load Forecasting ◽  
Software Tool ◽  
Regression Curve ◽  
Short Term ◽  
Load Curve ◽  
Consumption Data ◽  
Short Term Load Forecasting ◽  
System Operator ◽  
Linear Regression Curve

A software tool developed in Matlab for short-term load forecasting (STLF) is presented. Different forecasting methods such as artificial neural networks, multiple linear regression, curve fitting have been integrated into a stand-alone application with a graphical user interface. Real power consumption data have been used. They have been provided by the branches of the distribution system operator from the Southern-Western part of the Romanian Power System. This paper is an extended variant of [4].

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 Spanish Insular Electric Systems

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3645 ◽  
Author(s):  
Eduardo Caro ◽  
Jesús Juan
Keyword(s):  
Electric Power ◽  
Load Forecasting ◽  
Electric Power System ◽  
Balearic Islands ◽  
Energy Resources ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Short Term Load Forecasting ◽  
System Operator ◽  
Optimal Coordination

In any electric power system, the Transmission System Operator (TSO) requires the use of short-term load forecasting algorithms. These predictions are essential for appropriate planning of the energy resources and optimal coordination for the generation agents. This study focuses on the development of a prediction model to be applied to the ten main Spanish islands: seven insular systems in the Canary Islands, and three systems in the Balearic Islands. An exhaustive analysis is presented concerning both the estimation results and the forecasting accuracy, benchmarked against an alternative prediction software and a set of modified models. The developed models are currently being used by the Spanish TSO (Red Eléctrica de España, REE) to make hourly one-day-ahead forecasts of the electricity demand of insular systems.

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.

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