Data Science and Big Data in Energy Forecasting

This editorial summarizes the performance of the special issue entitled Data Science and Big Data in Energy Forecasting, which was published at MDPI’s Energies journal. The special issue took place in 2017 and accepted a total of 13 papers from 7 different countries. Electrical, solar and wind energy forecasting were the most analyzed topics, introducing new methods with applications of utmost relevance.Ministerio de Competitividad TIN2014-55894-C2-RMinisterio de Competitividad TIN2017-88209-C2-

Initialization Methods for Multiple Seasonal Holt-Winters Forecasting Models

[EN] The Holt-Winters models are one of the most popular forecasting algorithms. As well-known, these models are recursive and thus, an initialization value is needed to feed the model, being that a proper initialization of the Holt-Winters models is crucial for obtaining a good accuracy of the predictions. Moreover, the introduction of multiple seasonal Holt-Winters models requires a new development of methods for seed initialization and obtaining initial values. This work proposes new initialization methods based on the adaptation of the traditional methods developed for a single seasonality in order to include multiple seasonalities. Thus, new methods to initialize the level, trend, and seasonality in multiple seasonal Holt-Winters models are presented. These new methods are tested with an application for electricity demand in Spain and analyzed for their impact on the accuracy of forecasts. As a consequence of the analysis carried out, which initialization method to use for the level, trend, and seasonality in multiple seasonal Holt-Winters models with an additive and multiplicative trend is provided.Trull, O.; García-Díaz, JC.; Troncoso, A. (2020). Initialization Methods for Multiple Seasonal Holt-Winters Forecasting Models. Mathematics. 8(2):1-17. https://doi.org/10.3390/math8020268S11782Weron, R. (2014). Electricity price forecasting: A review of the state-of-the-art with a look into the future. International Journal of Forecasting, 30(4), 1030-1081. doi:10.1016/j.ijforecast.2014.08.008Taylor, J. W. (2003). Short-term electricity demand forecasting using double seasonal exponential smoothing. Journal of the Operational Research Society, 54(8), 799-805. doi:10.1057/palgrave.jors.2601589Taylor, J. W. (2010). Triple seasonal methods for short-term electricity demand forecasting. European Journal of Operational Research, 204(1), 139-152. doi:10.1016/j.ejor.2009.10.003Holt, C. C. (2004). Forecasting seasonals and trends by exponentially weighted moving averages. International Journal of Forecasting, 20(1), 5-10. doi:10.1016/j.ijforecast.2003.09.015Bowerman, B. L., Koehler, A., & Pack, D. J. (1990). Forecasting time series with increasing seasonal variation. Journal of Forecasting, 9(5), 419-436. doi:10.1002/for.3980090502Initializing the Holt–Winters Methodhttps://robjhyndman.com/hyndsight/hw-initialization/Rasmussen, R. (2004). On time series data and optimal parameters. Omega, 32(2), 111-120. doi:10.1016/j.omega.2003.09.013Trull, Ó., García-Díaz, J., & Troncoso, A. (2019). Application of Discrete-Interval Moving Seasonalities to Spanish Electricity Demand Forecasting during Easter. Energies, 12(6), 1083. doi:10.3390/en12061083Segura, J. V., & Vercher, E. (2001). A spreadsheet modeling approach to the Holt–Winters optimal forecasting. European Journal of Operational Research, 131(2), 375-388. doi:10.1016/s0377-2217(00)00062-xMakridakis, S., & Hibon, M. (1991). Exponential smoothing: The effect of initial values and loss functions on post-sample forecasting accuracy. International Journal of Forecasting, 7(3), 317-330. doi:10.1016/0169-2070(91)90005-gWilliams, D. W., & Miller, D. (1999). Level-adjusted exponential smoothing for modeling planned discontinuities. International Journal of Forecasting, 15(3), 273-289. doi:10.1016/s0169-2070(98)00083-

Stability of Multiple Seasonal Holt-Winters Models Applied to Hourly Electricity Demand in Spain

[EN] Electricity management and production depend heavily on demand forecasts made. Any mismatch between the energy demanded with respect to that produced supposes enormous losses for the consumer. Transmission System Operators use time series-based tools to forecast accurately the future demand and set the production program. One of the most effective and highly used methods are Holt-Winters. Recently, the incorporation of the multiple seasonal Holt-Winters methods has improved the accuracy of the predictions. These forecasts, depend greatly on the parameters with which the model is constructed. The forecasters need to deal with these parameters values when operating the model. In this article, the parameters space of the multiple seasonal Holt-Winters models applied to electricity demand in Spain is analysed and discussed. The parameters stability analysis leads to forecasters better understanding the behaviour of the predictions and managing their exploitation efficiently. The analysis addresses different time windows, depending on the period of the year as well as different training set sizes. The results show the influence of the calendar effect on these parameters and if it is necessary or not to update them in order to obtain a good accuracy over time.The authors would like to thank the Spanish Ministry of Economy and Competitiveness for the support under project TIN2017-8888209C2-1-R.Trull, Ó.; García-Díaz, JC.; Troncoso, A. (2020). Stability of Multiple Seasonal Holt-Winters Models Applied to Hourly Electricity Demand in Spain. Applied Sciences. 10(7):1-16. https://doi.org/10.3390/app10072630S11610

Pairwise gene GO-based measures for biclustering of high-dimensional expression data

Background: Biclustering algorithms search for groups of genes that share the same behavior under a subset of samples in gene expression data. Nowadays, the biological knowledge available in public repositories can be used to drive these algorithms to find biclusters composed of groups of genes functionally coherent. On the other hand, a distance among genes can be defined according to their information stored in Gene Ontology (GO). Gene pairwise GO semantic similarity measures report a value for each pair of genes which establishes their functional similarity. A scatter search-based algorithm that optimizes a merit function that integrates GO information is studied in this paper. This merit function uses a term that addresses the information through a GO measure. Results: The effect of two possible different gene pairwise GO measures on the performance of the algorithm is analyzed. Firstly, three well known yeast datasets with approximately one thousand of genes are studied. Secondly, a group of human datasets related to clinical data of cancer is also explored by the algorithm. Most of these data are high-dimensional datasets composed of a huge number of genes. The resultant biclusters reveal groups of genes linked by a same functionality when the search procedure is driven by one of the proposed GO measures. Furthermore, a qualitative biological study of a group of biclusters show their relevance from a cancer disease perspective. Conclusions: It can be concluded that the integration of biological information improves the performance of the biclustering process. The two different GO measures studied show an improvement in the results obtained for the yeast dataset. However, if datasets are composed of a huge number of genes, only one of them really improves the algorithm performance. This second case constitutes a clear option to explore interesting datasets from a clinical point of view.Ministerio de Economía y Competitividad TIN2014-55894-C2-

Improving Time Series Forecasting by Discovering Frequent Episodes in Sequences

This work aims to improve an existing time series forecasting algorithm –LBF– by the application of frequent episodes techniques as a complementary step to the model. When real-world time series are forecasted, there exist many samples whose values may be specially unexpected. By the combination of frequent episodes and the LBF algorithm, the new procedure does not make better predictions over these outliers but, on the contrary, it is able to predict the apparition of such atypical samples with a great accuracy. In short, this work shows how to detect the occurrence of anomalous samples in time series improving, thus, the general forecasting scheme. Moreover, this hybrid approach has been successfully tested on electricity-related time series

Recent Advances in Energy Time Series Forecasting

This editorial summarizes the performance of the special issue entitled Energy Time Series Forecasting, which was published in MDPI’s Energies journal. The special issue took place in 2016 and accepted a total of 21 papers from twelve different countries. Electrical, solar, or wind energy forecasting were the most analyzed topics, introducing brand new methods with very sound results.Ministerio de Economía y Competitividad TIN2014-55894-C2-

Evolutionary Metaheuristic for Biclustering based on Linear Correlations among Genes

A new measure to evaluate the quality of a bicluster is proposed in this paper. This measure is based on correlations among genes. Moreover, a new evolutionary metaheuristic based on Scatter Search, which uses this measure as the fitness function, is presented to obtain biclusters that contain groups de highly-correlated genes. Later, an analysis of the correlation matrix of these biclusters is made to select these groups of genes that define new biclusters with shifting and scaling patterns. Experimental results from human B cell lymphoma are presented.Ministerio de Ciencia e Innovación TIN2007-68084-C02Junta de Andalucía P07-TIC-0261

Biclustering of Gene Expression Data by Correlation-Based Scatter Search

BACKGROUND: The analysis of data generated by microarray technology is very useful to understand how the genetic information becomes functional gene products. Biclustering algorithms can determine a group of genes which are co-expressed under a set of experimental conditions. Recently, new biclustering methods based on metaheuristics have been proposed. Most of them use the Mean Squared Residue as merit function but interesting and relevant patterns from a biological point of view such as shifting and scaling patterns may not be detected using this measure. However, it is important to discover this type of patterns since commonly the genes can present a similar behavior although their expression levels vary in different ranges or magnitudes. METHODS: Scatter Search is an evolutionary technique that is based on the evolution of a small set of solutions which are chosen according to quality and diversity criteria. This paper presents a Scatter Search with the aim of finding biclusters from gene expression data. In this algorithm the proposed fitness function is based on the linear correlation among genes to detect shifting and scaling patterns from genes and an improvement method is included in order to select just positively correlated genes. RESULTS: The proposed algorithm has been tested with three real data sets such as Yeast Cell Cycle dataset, human B-cells lymphoma dataset and Yeast Stress dataset, finding a remarkable number of biclusters with shifting and scaling patterns. In addition, the performance of the proposed method and fitness function are compared to that of CC, OPSM, ISA, BiMax, xMotifs and Samba using Gene the Ontology Database

Correlation–Based Scatter Search for Discovering Biclusters from Gene Expression Data

Scatter Search is an evolutionary method that combines ex isting solutions to create new offspring as the well–known genetic algo rithms. This paper presents a Scatter Search with the aim of finding biclusters from gene expression data. However, biclusters with certain patterns are more interesting from a biological point of view. Therefore, the proposed Scatter Search uses a measure based on linear correlations among genes to evaluate the quality of biclusters. As it is usual in Scatter Search methodology an improvement method is included which avoids to find biclusters with negatively correlated genes. Experimental results from yeast cell cycle and human B-cell lymphoma datasets are reported showing a remarkable performance of the proposed method and measureMinisterio de Ciencia y Tecnología TIN2007-68084-C00Junta de Andalucía P07-TIC-0261