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

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.

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A Multifactorial Framework for Short-Term Load Forecasting System as Well as the Jinan’s Case Study

IEEE Access ◽

10.1109/access.2020.3036675 ◽

2020 ◽

Vol 8 ◽

pp. 203086-203096

Author(s):

Ya Gao ◽

Yong Fang ◽

Huanhe Dong ◽

Yuan Kong

Keyword(s):

Load Forecasting ◽

Short Term ◽

Short Term Load Forecasting ◽

Forecasting System

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

Energies ◽

10.3390/en14010095 ◽

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%.

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

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.614-615.866 ◽

2012 ◽

Vol 614-615 ◽

pp. 866-869 ◽

Cited By ~ 1

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.

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A Multifactorial Short-Term Load Forecasting Model Combined With Periodic and Non-Periodic Features - A Case Study of Qingdao, China

IEEE Access ◽

10.1109/access.2020.2986031 ◽

2020 ◽

Vol 8 ◽

pp. 67416-67425

Author(s):

Huanhe Dong ◽

Ya Gao ◽

Xiangjun Meng ◽

Yong Fang

Keyword(s):

Load Forecasting ◽

Forecasting Model ◽

Short Term ◽

Short Term Load Forecasting

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A CNN Based Model for Short-Term Load Forecasting: A Real Case Study on the Romanian Power System

2020 55th International Universities Power Engineering Conference (UPEC) ◽

10.1109/upec49904.2020.9209768 ◽

2020 ◽

Author(s):

Andrei M. Tudose ◽

Dorian O. Sidea ◽

Irina I. Picioroaga ◽

Valentin A. Boicea ◽

Constatin Bulac

Keyword(s):

Power System ◽

Load Forecasting ◽

Real Case ◽

Short Term ◽

Short Term Load Forecasting

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A load curve based fuzzy modeling technique for short-term load forecasting

Fuzzy Sets and Systems ◽

10.1016/s0165-0114(02)00211-7 ◽

2003 ◽

Vol 135 (2) ◽

pp. 279-303 ◽

Cited By ~ 17

Author(s):

S.E. Papadakis ◽

J.B. Theocharis ◽

A.G. Bakirtzis

Keyword(s):

Load Forecasting ◽

Fuzzy Modeling ◽

Modeling Technique ◽

Short Term ◽

Load Curve ◽

Short Term Load Forecasting

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Short-Term Load Forecasting for Electric Vehicle Charging Stations Based on Deep Learning Approaches

Applied Sciences ◽

10.3390/app9091723 ◽

2019 ◽

Vol 9 (9) ◽

pp. 1723 ◽

Cited By ~ 12

Author(s):

Juncheng Zhu ◽

Zhile Yang ◽

Yuanjun Guo ◽

Jiankang Zhang ◽

Huikun Yang

Keyword(s):

Deep Learning ◽

Power Systems ◽

Unit Commitment ◽

Combustion Engine ◽

Load Forecasting ◽

Learning Approaches ◽

Short Term ◽

Load Curve ◽

Short Term Load Forecasting ◽

Gated Recurrent Units

Short-term load forecasting is a key task to maintain the stable and effective operation of power systems, providing reasonable future load curve feeding to the unit commitment and economic load dispatch. In recent years, the boost of internal combustion engine (ICE) based vehicles leads to the fossil fuel shortage and environmental pollution, bringing significant contributions to the greenhouse gas emissions. One of the effective ways to solve problems is to use electric vehicles (EVs) to replace the ICE based vehicles. However, the mass rollout of EVs may cause severe problems to the power system due to the huge charging power and stochastic charging behaviors of the EVs drivers. The accurate model of EV charging load forecasting is, therefore, an emerging topic. In this paper, four featured deep learning approaches are employed and compared in forecasting the EVs charging load from the charging station perspective. Numerical results show that the gated recurrent units (GRU) model obtains the best performance on the hourly based historical data charging scenarios, and it, therefore, provides a useful tool of higher accuracy in terms of the hourly based short-term EVs load forecasting.

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