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 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%.

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 Macromodelling as an Approach to Short-Term Load Forecasting of Electric Power System Objects

2017 ◽  
Vol 7 (1) ◽  
pp. 25-32
Author(s):  
Oksana Hoholyuk ◽  
◽  
Yuriy Kozak ◽  
Taras Nakonechnyy ◽  
Petro Stakhiv ◽  
...  
Keyword(s):  
Power System ◽  
Electric Power ◽  
Load Forecasting ◽  
Electric Power System ◽  
Short Term ◽  
Short Term Load Forecasting

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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