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2. Short-Term Forecasting of Electric Loads Using Nonlinear Autoregressive Artificial Neural Networks with Exogenous Vector Inputs. (2017). Asfour, Shihab ; Buitrago, Jaime .
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6. An evaluation of methods for very short-term load forecasting using minute-by-minute British data. (2008). Taylor, James W..
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7. Modeling and forecasting short-term electricity load: A comparison of methods with an application to Brazilian data. (2008). Medeiros, Marcelo ; Soares, Lacir J..
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1. Towards seasonal adjustment of infra-monthly time series with JDemetra+. (2023). Webel, Karsten ; Smyk, Anna.
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2. Enhancing Energy System Models Using Better Load Forecasts. (2023). Mobius, Thomas ; Grothe, Oliver ; Watermeyer, Mira ; Musgens, Felix.
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3. A review of some recent developments in the modelling and seasonal adjustment of infra-monthly time series. (2022). Webel, Karsten.
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4. A proposal for measuring and comparing seasonal variations in hourly economic time series. (2022). Cáceres-Hernández, José Juan ; Caceres-Hernandez, Jose Juan ; Hernandez-Martin, Jonay ; Martin-Rodriguez, Gloria.
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5. A Bayesian Model to Forecast the Time Series Kinetic Energy Data for a Power System. (2021). Gonzalez-Longatt, Francisco ; Ghimire, Bishal ; Shrestha, Ashish.
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6. One-day-ahead electricity demand forecasting in holidays using discrete-interval moving seasonalities. (2021). Trull, Oscar ; Garcia-Diaz, Carlos J ; Troncoso, Alicia.
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7. Residual electricity demand: An empirical investigation. (2021). Molnár, Peter ; Lyócsa, Štefan ; Lyocsa, Tefan ; Molnar, Peter ; Catherine, Linh Phuong.
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8. Use of Available Daylight to Improve Short-Term Load Forecasting Accuracy. (2020). Valero, Sergio ; Lopez, Miguel ; Senabre, Carolina ; Sans, Carlos.
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9. Point and Interval Forecasting of Zonal Electricity Prices and Demand Using Heteroscedastic Models: The IPEX Case. (2020). Bernardi, Mauro ; Lisi, Francesco.
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10. Short-Term Load Forecasting for Spanish Insular Electric Systems. (2020). Juan, Jesus ; Caro, Eduardo.
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11. Daily retail demand forecasting using machine learning with emphasis on calendric special days. (2020). Huber, Jakob ; Stuckenschmidt, Heiner.
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12. Novel deep supervised ML models with feature selection approach for large-scale utilities and buildings short and medium-term load requirement forecasts. (2020). Ahmad, Tanveer ; Zhang, Hongcai.
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13. Daylight effect on the electricity demand in Spain and assessment of Daylight Saving Time policies. (2020). Lopez, Miguel.
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14. Electricity Forecasting Improvement in a Destination Using Tourism Indicators. (2019). Trull, Oscar ; Garcia-Diaz, Carlos J ; Peiro-Signes, Angel.
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15. Classification of Special Days in Short-Term Load Forecasting: The Spanish Case Study. (2019). Valero, Sergio ; Lopez, Miguel ; Senabre, Carolina ; Sans, Carlos.
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16. Application of Discrete-Interval Moving Seasonalities to Spanish Electricity Demand Forecasting during Easter. (2019). Trull, Oscar ; Garcia-Diaz, Carlos J ; Troncoso, Alicia.
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17. On the Influence of Renewable Energy Sources in Electricity Price Forecasting in the Iberian Market. (2019). Lemus-Zuiga, Lenin G ; Iranzo-Sanchez, Javier ; Urchueguia, Javier F ; Onaindia, Eva ; Aineto, Diego.
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18. A data-driven framework for predicting weather impact on high-volume low-margin retail products. (2019). Verstraete, Gylian ; Aghezzaf, El-Houssaine ; Desmet, Bram.
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19. Empirical Comparison of Neural Network and Auto-Regressive Models in Short-Term Load Forecasting. (2018). Valero, Sergio ; Lopez, Miguel ; Senabre, Carolina ; Sans, Carlos.
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20. A Practical Formulation for Ex-Ante Scheduling of Energy and Reserve in Renewable-Dominated Power Systems: Case Study of the Iberian Peninsula. (2018). Dominguez, Ruth ; Zarate-Miano, Rafael ; Carrion, Miguel.
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21. Rule-based autoregressive moving average models for forecasting load on special days: A case study for France. (2018). Arora, Siddharth ; Taylor, James W.
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22. Short-run electricity load forecasting with combinations of stationary wavelet transforms. (2018). Fouquau, Julien ; Bessec, Marie.
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23. Functional Autoregressive Models: An Application to Brazilian Hourly Electricity Load. (2017). Vaz, Lucelia Viviane ; da Silveira, Getulio Borges.
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24. Demand for electricity and weather conditions: Nonparametric analysis. (2017). Shenkman, Evgeniya ; Ozhegov, Evgeniy ; Popova, Evgeniya.
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25. A review of the decomposition methodology for extracting and identifying the fluctuation characteristics in electricity demand forecasting. (2017). Shao, Zhen ; Yang, Shan-Lin ; Zhou, Kai-Le ; Chao, FU.
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26. Modelling weather effects for impact analysis of residential time-of-use electricity pricing. (2017). Miller, Reid ; Golab, Lukasz ; Rosenberg, Catherine.
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27. Forecasting quantiles of day-ahead electricity load. (2017). Hurn, Stan ; Clements, Adam ; Li, Z.
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28. Collateral effects of liberalisation: Metering, losses, load profiles and cost settlement in Spain’s electricity system. (2016). Trujillo-Baute, Elisa ; Costa-Campi, Maria ; Batalla-Bejerano, Joan.
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29. Forecasting day-ahead electricity load using a multiple equation time series approach. (2016). Hurn, Stan ; Clements, Adam ; Li, Z.
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30. Electricity consumption modelling: A case of Germany. (2016). Molnár, Peter ; Molnar, Peter ; Catherine, Linh Phuong ; Lin, Kuan-Heng.
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31. Heat wave, electricity rationing, and trade-offs between environmental gains and economic losses: The example of Shanghai. (2016). Tian, Zhan ; Liu, Xiaochen ; Gu, Tingting ; Sun, Laixiang ; Feng, Kuishuang ; Zhong, Honglin ; Liang, Zhuoran.
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32. Multiple seasonal cycles forecasting model: the Italian electricity demand. (2015). Petrella, Lea ; Bernardi, Mauro.
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33. Forecasting day-ahead electricity load using a multiple equation time series approach. (2015). Hurn, Stan ; Clements, Adam ; Li, Zili.
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34. Efficient modeling and forecasting of electricity spot prices. (2015). Husmann, Sven ; Steinert, Rick ; Ziel, Florian.
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35. Forecasting day-ahead electricity load using a multiple equation time series approach. (2014). A. S Hurn, ; A. E Clements, .
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36. Efficient Modeling and Forecasting of the Electricity Spot Price. (2014). Steinert, Rick ; Ziel, Florian.
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37. Assessing Tolerance-Based Robust Short-Term Load Forecasting in Buildings. (2013). Penya, Yoseba K. ; Bretos, Oscar ; Prieto, Juan ; Fernandez, Ivan ; Borges, Cruz E..
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38. Modeling special-day effects for forecasting intraday electricity demand. (2013). Kim, Myung Suk.
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39. The power of weather. (2012). Zhou, Chen ; Ravazzolo, Francesco ; Huurman, Christian .
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40. Inflation forecasting in Angola: a fractional approach. (2012). Gil-Alana, Luis ; Barros, Carlos P..
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41. Estimation of tourism-induced electricity consumption: The case study of Balearics Islands, Spain. (2011). Rossello, Jaume ; Bakhat, Mohcine.
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42. Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models. (2010). Nabney, Ian T. ; Nguyen, Hang T..
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43. Triple seasonal methods for short-term electricity demand forecasting. (2010). Taylor, James W..
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44. Forecasting electricity demand in Japan: A Bayesian spatial autoregressive ARMA approach. (2010). Ohtsuka, Yoshihiro ; Oga, Takashi ; Kakamu, Kazuhiko.
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45. Factors affecting electricity demand in Athens, Greece and London, UK: A comparative assessment. (2009). Majithia, S. ; Petrakis, M. ; Giannakopoulos, C. ; Psiloglou, B. E..
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46. Frequency domain methods applied to forecasting electricity markets. (2009). Trapero, Juan R. ; Pedregal, Diego J..
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47. A nonlinear mixed effects model for the prediction of natural gas consumption by individual customers. (2008). Konar, Ondrej ; Mal, Marek ; Brabec, Marek ; Pelikan, Emil.
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48. An evaluation of methods for very short-term load forecasting using minute-by-minute British data. (2008). Taylor, James W..
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49. Modeling and forecasting short-term electricity load: A comparison of methods with an application to Brazilian data. (2008). Medeiros, Marcelo ; Soares, Lacir J..
    In: International Journal of Forecasting.
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50. Input space to neural network based load forecasters. (2008). Ferreira, Vitor H. ; Velasquez, Roberto M. G., ; Alves da Silva, Alexandre P., .
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