Adaptability verification and application of the t-distribution in short-term load forecasting error analysis

2014 ◽  
Author(s):  
Xing Tong ◽  
Qixin Chen ◽  
Jie Fan ◽  
Qingguo Yan ◽  
Chongqing Kang
Keyword(s):  
Error Analysis ◽  
Load Forecasting ◽  
Short Term ◽  
Forecasting Error ◽  
Short Term Load Forecasting ◽  
T Distribution

The Application of Chaotic Particle Swarm Optimization Algorithm in Power System Load Forecasting

2012 ◽  
Vol 614-615 ◽  
pp. 866-869 ◽  
Author(s):  
Yu Hong Zhao ◽  
Xue Cheng Zhao ◽  
Wei Cheng
Keyword(s):  
Optimization Algorithm ◽  
Load Forecasting ◽  
Support Vector ◽  
Short Term ◽  
Swarm Optimization ◽  
Forecasting Error ◽  
Parameters Selection ◽  
Short Term Load Forecasting ◽  
Search Speed ◽  
Chaotic Particle Swarm Optimization

The support vector machine (SVM) has been successfully applied in the short-term load forecasting area, but its learning and generalization ability depends on a proper setting of its parameters. In order to improve forecasting accuracy, aiming at the disadvantages like man-made blindness in the parameters selection of SVM, In this paper, the chaos theory was applied to the PSO (particles swarm optimization) algorithm in order to cope with the problems such as low search speed and local optimization. Finally, we used it to optimize the support vector machines of short-term load forecasting model. Through the analysis of the daily forecasting results, it is shown that the proposed method could reduce modeling error and forecasting error of SVM model effectively and has better performance than general methods.

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 Classification of Special Days in Short-Term Load Forecasting: The Spanish Case Study

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1253 ◽  
Author(s):  
Miguel López ◽  
Carlos Sans ◽  
Sergio Valero ◽  
Carolina Senabre
Keyword(s):  
Load Forecasting ◽  
Short Term ◽  
Load Curve ◽  
Electric System ◽  
Forecasting Error ◽  
Social Events ◽  
Short Term Load Forecasting ◽  
Forecasting System

Short-Term Load Forecasting is a very relevant aspect in managing, operating or participating an electric system. From system operators to energy producers and retailers knowing the electric demand in advance with high accuracy is a key feature for their business. The load series of a given system presents highly repetitive daily, weekly and yearly patterns. However, other factors like temperature or social events cause abnormalities in this otherwise periodic behavior. In order to develop an effective load forecasting system, it is necessary to understand and model these abnormalities because, in many cases, the higher forecasting error typical of these special days is linked to the larger part of the losses related to load forecasting. This paper focuses on the effect that several types of special days have on the load curve and how important it is to model these behaviors in detail. The paper analyzes the Spanish national system and it uses linear regression to model the effect that social events like holidays or festive periods have on the load curve. The results presented in this paper show that a large classification of events is needed in order to accurately model all the events that may occur in a 7-year period.

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.

scholarly journals An Attention-Based Multilayer GRU Model for Multistep-Ahead Short-Term Load Forecasting

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1639
Author(s):  
Seungmin Jung ◽  
Jihoon Moon ◽  
Sungwoo Park ◽  
Eenjun Hwang
Keyword(s):  
Power Consumption ◽  
Prediction Models ◽  
Short Term Memory ◽  
Load Forecasting ◽  
Input Sequence ◽  
Short Term ◽  
Performance Improvements ◽  
Short Term Load Forecasting ◽  
Significant Performance ◽  
Input Variables

Recently, multistep-ahead prediction has attracted much attention in electric load forecasting because it can deal with sudden changes in power consumption caused by various events such as fire and heat wave for a day from the present time. On the other hand, recurrent neural networks (RNNs), including long short-term memory and gated recurrent unit (GRU) networks, can reflect the previous point well to predict the current point. Due to this property, they have been widely used for multistep-ahead prediction. The GRU model is simple and easy to implement; however, its prediction performance is limited because it considers all input variables equally. In this paper, we propose a short-term load forecasting model using an attention based GRU to focus more on the crucial variables and demonstrate that this can achieve significant performance improvements, especially when the input sequence of RNN is long. Through extensive experiments, we show that the proposed model outperforms other recent multistep-ahead prediction models in the building-level power consumption forecasting.

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