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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%.
Miguel López; Sergio Valero; Carlos Sans; Carolina Senabre. Use of Available Daylight to Improve Short-Term Load Forecasting Accuracy. Energies 2020, 14, 95 .
AMA StyleMiguel López, Sergio Valero, Carlos Sans, Carolina Senabre. Use of Available Daylight to Improve Short-Term Load Forecasting Accuracy. Energies. 2020; 14 (1):95.
Chicago/Turabian StyleMiguel López; Sergio Valero; Carlos Sans; Carolina Senabre. 2020. "Use of Available Daylight to Improve Short-Term Load Forecasting Accuracy." Energies 14, no. 1: 95.
Daylight Saving Time (DST) policies have been in use since early in the 20th century. However, their energy saving effect is under review. The generalization of LED lighting has reduced the impact of lighting energy on the total energy consumption and, therefore, the effect of DST has been reduced. Nevertheless, in order to design an effective new policy in this aspect, it is necessary to understand how total electricity consumption would be affected by it but also how the load daily profile would change. This paper proposes an hourly load model that quantifies the effect of daylight on electricity consumption and simulates the effect of different DST policies. The model is applied to the inland Spanish electricity system and the three scenarios more likely to be implemented if current DST policy is changed: year round winter time (UTC+1), year round summer time (UTC+2) and keep DST but change clock 1 h back (GMT). The results show how changes in sunrise and sunset times affect daily load profiles. They provide overall, monthly and hourly energy savings for each scenario that are necessary for a well-informed DST policy design and implementation.
Miguel López. Daylight effect on the electricity demand in Spain and assessment of Daylight Saving Time policies. Energy Policy 2020, 140, 111419 .
AMA StyleMiguel López. Daylight effect on the electricity demand in Spain and assessment of Daylight Saving Time policies. Energy Policy. 2020; 140 ():111419.
Chicago/Turabian StyleMiguel López. 2020. "Daylight effect on the electricity demand in Spain and assessment of Daylight Saving Time policies." Energy Policy 140, no. : 111419.
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.
Miguel Lopez; Carlos Sans; Sergio Valero; Carolina Senabre. Classification of Special Days in Short-Term Load Forecasting: The Spanish Case Study. Energies 2019, 12, 1253 .
AMA StyleMiguel Lopez, Carlos Sans, Sergio Valero, Carolina Senabre. Classification of Special Days in Short-Term Load Forecasting: The Spanish Case Study. Energies. 2019; 12 (7):1253.
Chicago/Turabian StyleMiguel Lopez; Carlos Sans; Sergio Valero; Carolina Senabre. 2019. "Classification of Special Days in Short-Term Load Forecasting: The Spanish Case Study." Energies 12, no. 7: 1253.
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.
Miguel López; Carlos Sans; Sergio Valero; Carolina Senabre. Empirical Comparison of Neural Network and Auto-Regressive Models in Short-Term Load Forecasting. Energies 2018, 11, 2080 .
AMA StyleMiguel López, Carlos Sans, Sergio Valero, Carolina Senabre. Empirical Comparison of Neural Network and Auto-Regressive Models in Short-Term Load Forecasting. Energies. 2018; 11 (8):2080.
Chicago/Turabian StyleMiguel López; Carlos Sans; Sergio Valero; Carolina Senabre. 2018. "Empirical Comparison of Neural Network and Auto-Regressive Models in Short-Term Load Forecasting." Energies 11, no. 8: 2080.
This paper presents the implementation of a new online real-time hybrid load-forecasting model based on an autoregressive model and neural networks. This new system is currently running at the Spanish Transport System Operator (REE) and provides an hourly forecast for the current day and the next nine days timely every hour for the national system as well as 18 regions of Spain. These requirements impose a heavy computational burden that needs to be considered during the design phase. The system is developed to improve forecasting accuracy specifically on difficult days like hot, cold and special days. In order to achieve this goal, a deep analysis of the temperature series from 59 stations is made for each region and the relevant series are included individually in the model. Special days are also analyzed and a thorough classification of days is proposed for the Spanish national and regional system. The model is designed and tested with data from 2005 to 2015. The results provided for the period from December 2014 to October 2015 show how the addition of the proposed model to the TSO’s ensemble causes a 5% RMSE overall error reduction and a 15% reduction on the 59 difficult days considered in the testing period.
Miguel López; Sergio Valero; Ana Rodriguez; Iago Veiras; Carolina Senabre. New online load forecasting system for the Spanish Transport System Operator. Electric Power Systems Research 2017, 154, 401 -412.
AMA StyleMiguel López, Sergio Valero, Ana Rodriguez, Iago Veiras, Carolina Senabre. New online load forecasting system for the Spanish Transport System Operator. Electric Power Systems Research. 2017; 154 ():401-412.
Chicago/Turabian StyleMiguel López; Sergio Valero; Ana Rodriguez; Iago Veiras; Carolina Senabre. 2017. "New online load forecasting system for the Spanish Transport System Operator." Electric Power Systems Research 154, no. : 401-412.
Natividad Hernández Muñoz; Miguel López García. ANÁLISIS DE LAS RELACIONES SEMÁNTICAS A TRAVÉS DE UNA TAREA DE LIBRE ASOCIACIÓN EN ESPAÑOL CON MAPAS AUTO-ORGANIZADOS. RLA. Revista de Lingüística Teórica y Aplicada 2014, 52, 189 -212.
AMA StyleNatividad Hernández Muñoz, Miguel López García. ANÁLISIS DE LAS RELACIONES SEMÁNTICAS A TRAVÉS DE UNA TAREA DE LIBRE ASOCIACIÓN EN ESPAÑOL CON MAPAS AUTO-ORGANIZADOS. RLA. Revista de Lingüística Teórica y Aplicada. 2014; 52 (2):189-212.
Chicago/Turabian StyleNatividad Hernández Muñoz; Miguel López García. 2014. "ANÁLISIS DE LAS RELACIONES SEMÁNTICAS A TRAVÉS DE UNA TAREA DE LIBRE ASOCIACIÓN EN ESPAÑOL CON MAPAS AUTO-ORGANIZADOS." RLA. Revista de Lingüística Teórica y Aplicada 52, no. 2: 189-212.
The use of neural networks in load forecasting has been a popular research topic over the last decade. However, the use of Kohonen's self-organizing maps (SOM) for this purpose remains yet mostly unexplored. This paper presents a forecasting model based on this particular type of neural network. The scope of this study is not only to prove that SOM neural networks can be effectively used in load forecasting but to provide a deep and thorough analysis of the prediction and a real-world application. The data used to assess the validity of the model corresponds to Spain energy consumption from 2001 to 2010. Also meteorological data from this period has been used. The analysis comprises the study of the significance of different meteorological variables, the relevance of these meteorological data when recent load values are used as input and the effect of using different patterns to select the days to train the map. In addition, the evaluation of the frequency components of the data has provided an explanation to why apparently similar data sets allow different forecasting performances of the model. In order to build an application to the Spanish electricity market, the model was adjusted to timely forecast a load profile for each session of the daily and intra-daily markets. These forecasts are intended as an input to a decision support system for any commercializing company bidding on the market.
Miguel Lopez; S. Valero; C. Senabre; J. Aparicio; Antonio Gabaldon. Application of SOM neural networks to short-term load forecasting: The Spanish electricity market case study. Electric Power Systems Research 2012, 91, 18 -27.
AMA StyleMiguel Lopez, S. Valero, C. Senabre, J. Aparicio, Antonio Gabaldon. Application of SOM neural networks to short-term load forecasting: The Spanish electricity market case study. Electric Power Systems Research. 2012; 91 ():18-27.
Chicago/Turabian StyleMiguel Lopez; S. Valero; C. Senabre; J. Aparicio; Antonio Gabaldon. 2012. "Application of SOM neural networks to short-term load forecasting: The Spanish electricity market case study." Electric Power Systems Research 91, no. : 18-27.