A Survey on Data Mining Techniques Applied to Energy Time Series Forecasting

Data mining has become an essential tool during the last decade to analyze large sets of data. The variety of techniques it includes and the successful results obtained in many application fields, make this family of approaches powerful and widely used. In particular, this work explores the application of these techniques to time series forecasting. Although classical statistical-based methods provides reasonably good results, the result of the application of data mining outperforms those of classical ones. Hence, this work faces two main challenges: (i) to provide a compact mathematical formulation of the mainly used techniques; (ii) to review the latest works of time series forecasting and, as case study, those related to electricity price and demand markets.Ministerio de Economía y Competitividad TIN2014-55894-C2-RJunta de Andalucía P12- TIC-1728Universidad Pablo de Olavide APPB81309

A novel tree-based algorithm to discover seismic patterns in earthquake catalogs

A novel methodology is introduced in this research study to detect seismic precursors. Based on an existing approach, the new methodology searches for patterns in the historical data. Such patterns may contain statistical or soil dynamics information. It improves the original version in several aspects. First, new seismicity indicators have been used to characterize earthquakes. Second, a machine learning clustering algorithm has been applied in a very flexible way, thus allowing the discovery of new data groupings. Third, a novel search strategy is proposed in order to obtain non-overlapped patterns. And, fourth, arbitrary lengths of patterns are searched for, thus discovering long and short-term behaviors that may influence in the occurrence of medium-large earthquakes. The methodology has been applied to seven different datasets, from three different regions, namely the Iberian Peninsula, Chile and Japan. Reported results show a remarkable improvement with respect to the former version, in terms of all evaluated quality measures. In particular, the number of false positives has decreased and the positive predictive values increased, both of them in a very remarkable manner.Ministerio de Ciencia y Tecnología TIN2011-28956-C00Junta de Andalucía P12-TIC-1728Instituto Ramón y Cajal (RYC) RYC-2012-1198

A novel ensemble method for electric vehicle power consumption forecasting: Application to the Spanish system

The use of electric vehicle across the world has become one of the most challenging issues for environmental policies. The galloping climate change and the expected running out of fossil fuels turns the use of such non-polluting cars into a priority for most developed countries. However, such a use has led to major concerns to power companies, since they must adapt their generation to a new scenario, in which electric vehicles will dramatically modify the curve of generation. In this paper, a novel approach based on ensemble learning is proposed. In particular, ARIMA, GARCH and PSF algorithms' performances are used to forecast the electric vehicle power consumption in Spain. It is worth noting that the studied time series of consumption is non-stationary and adds difficulties to the forecasting process. Thus, an ensemble is proposed by dynamically weighting all algorithms over time. The proposal presented has been implemented for a real case, in particular, at the Spanish Control Centre for the Electric Vehicle. The performance of the approach is assessed by means of WAPE, showing robust and promising results for this research field.Ministerio de Economía y Competitividad Proyectos ENE2016-77650-R, PCIN-2015-04 y TIN2017-88209-C2-R

A combined estimator using TEC and b-value for large earthquake prediction

[EN] Ionospheric anomalies have been shown to occur a few days before several large earthquakes. The published works normally address examples limited in time (a single event or few of them) or space (a particular geographic area), so that a clear method based on these anomalies which consistently yields the place and magnitude of the forthcoming earthquake, anytime and anywhere on earth, has not been presented so far. The current research is aimed at prediction of large earthquakes, that is with magnitude M-w 7 or higher. It uses as data bank all significant earthquakes occurred worldwide in the period from January 1, 2011 to December 31, 2018. The first purpose of the research is to improve the use of ionospheric anomalies in the form of TEC grids for earthquake prediction. A space-time TEC variation estimator especially designed for earthquake prediction will show the advantages with respect to the use of simple TEC values. Further, taking advantage of the well-known predictive abilities of the Gutenberg-Richter law's b-value, a combined estimator based on both TEC anomalies and b-values will be designed and shown to improve prediction performance even more.Baselga Moreno, S. (2020). A combined estimator using TEC and b-value for large earthquake prediction. Acta Geodaetica et Geophysica Hungarica. 55(1):63-82. https://doi.org/10.1007/s40328-019-00281-5S6382551Abordán A, Szabó NP (2018) Metropolis algorithm driven factor analysis for lithological characterization of shallow marine sediments. Acta Geod Geophys 53:189–199. https://doi.org/10.1007/s40328-017-0210-zAkhoondzadeh M, Saradjian MR (2011) TEC variations analysis concerning Haiti (January 12, 2010) and Samoa (September 29, 2009) earthquakes. Adv Space Res 47(1):94–104. https://doi.org/10.1016/j.asr.2010.07.024Asencio-Cortés G, Morales-Esteban A, Shang X, Martínez-Álvarez F (2018) Earthquake prediction in California using regression algorithms and cloud-based big data infrastructure. Comput Geosci 115:198–210. https://doi.org/10.1016/j.cageo.2017.10.011Baselga S (2018) Fibonacci lattices for the evaluation and optimization of map projections. Comput Geosci 117:1–8. https://doi.org/10.1016/j.cageo.2018.04.012Baselga S (2019) TestGrids: evaluating and optimizing map projections. J Surv Eng 144(3):04019004Berényi KA, Barta V, Kis Á (2018) Midlatitude ionospheric F2-layer response to eruptive solar events-caused geomagnetic disturbances over Hungary during the maximum of the solar cycle 24: a case study. Adv Space Res 61(5):1230–1243. https://doi.org/10.1016/j.asr.2017.12.021Biswas A, Sharma SP (2017) Interpretation of self-potential anomaly over 2-D inclined thick sheet structures and analysis of uncertainty using very fast simulated annealing global optimization. Acta Geod Geophys 52:439–455. https://doi.org/10.1007/s40328-016-0176-2Borgohain JM, Borah K, Biswas R, Bora DK (2018) Seismic b-value anomalies prior to the 3rd January 2016, Mw = 6.7 Manipur earthquake of northeast India. J Asian Earth Sci 154:42–48. https://doi.org/10.1016/j.jseaes.2017.12.013Buonsanto M (1999) Ionospheric storms—a review. Space Sci Rev 88:563–601. https://doi.org/10.1023/A:1005107532631Buskirk RE, Frohlich CL, Latham GV (1981) Unusual animal behavior before earthquakes: a review of possible sensory mechanisms. Rev Geophys 19:247–270. https://doi.org/10.1029/RG019i002p00247Dobrovolsky IR, Zubkov SI, Myachkin VI (1979) Estimation of the size of earthquake preparation zones. Pure appl Geophys 117:1025–1044. https://doi.org/10.1007/BF00876083Dogan U, Ergintav S, Skone S, Arslan N, Oz D (2011) Monitoring of the ionosphere TEC variations during the 17th August 1999 Izmit earthquake using GPS data. Earth Planets Space 63(12):1183–1192. https://doi.org/10.5047/eps.2011.07.020Florido E, Martínez-Álvarez F, Morales-Esteban A, Reyes J, Aznarte-Mellado JL (2015) Detecting precursory patterns to enhance earthquake prediction in Chile. Comput Geosci 76:112–120. https://doi.org/10.1016/j.cageo.2014.12.002Florido E, Asencio-Cortés G, Aznarte JL, Rubio-Escudero C, Martínez-Álvarez F (2018) A novel tree-based algorithm to discover seismic patterns in earthquake catalogs. Comput Geosci 115:96–104. https://doi.org/10.1016/j.cageo.2018.03.005Freund FT, Kulahci IG, Cyr G, Ling J, Winnick M, Tregloan-Reed J, Freund MM (2009) Air ionization at rock surfaces and pre-earthquake signals. J Atmos Sol Terr Phys 71(17–18):1824–1834. https://doi.org/10.1016/j.jastp.2009.07.013Gopinath S, Prince PR (2018) Nonextensive and distance-based entropy analysis on the influence of sunspot variability in magnetospheric dynamics. Acta Geod Geophys 53:639–659. https://doi.org/10.1007/s40328-018-0235-yGrant RA, Halliday T (2010) Predicting the unpredictable; evidence of pre-seismic anticipatory behaviour in the common toad. J Zool 281:263–271. https://doi.org/10.1111/j.1469-7998.2010.00700.xGrant RA, Halliday T, Balderer WP, Leuenberger F, Newcomer M, Cyr G, Freund FT (2011) Ground water chemistry changes before major earthquakes and possible effects on animals. Int J Environ Res Public Health 8:1936–1956. https://doi.org/10.3390/ijerph8061936Guo J, Yu H, Li W, Liu X, Kong Q, Zhao C (2017) Total electron content anomalies before Mw 6.0 + earthquakes in the seismic zone of southwest China between 2001 and 2013. J Test Eval 45(1):131–139. https://doi.org/10.1520/JTE20160032International GNSS Service (2019) IGS products. https://www.igs.org/products. Accessed 5 May 2019Kane RP (2005) Ionospheric foF2 anomalies during some intense geomagnetic storms. Ann Geophys 23:2487–2499. https://doi.org/10.5194/angeo-23-2487-2005Kulhanek O, Persson L, Nuannin P (2018) Variations of b-values preceding large earthquakes in the shallow subduction zones of Cocos and Nazca plates. J South Am Earth Sci 82:207–214. https://doi.org/10.1016/j.jsames.2018.01.005Lin JW (2010) Ionospheric total electron content (TEC) anomalies associated with earthquakes through Karhunen–Loéve Transform (KLT). Terr Atmos Ocean Sci 21(2):253–265. https://doi.org/10.3319/TAO.2009.06.11.01(T)Lin JW (2011) Latitude-time total electron content anomalies as precursors to Japan’s large earthquakes associated with principal component analysis. Int J Geophys. https://doi.org/10.1155/2011/763527Liu JY, Chen YI, Chuo YJ, Chen CS (2006) A statistical investigation of preearthquake ionospheric anomaly. J Geophys Res 111:A05304. https://doi.org/10.1029/2005JA011333Liu JY, Chen YI, Chen CH, Liu CY, Chen CY, Nishihashi M, Li JZ, Xia YQ, Oyama KI, Hattori K, Lin CH (2009) Seismoionospheric GPS total electron content anomalies observed before the 12 May 2008 Mw7.9 Wenchuan earthquake. J Geophys Res 114:A04320. https://doi.org/10.1029/2008JA013698Nuannin P, Kulhanek O, Persson L (2005) Spatial and temporal b value anomalies preceding the devastating off coast of NW Sumatra earthquake of December 26, 2004. Geophys Res Lett 32:L11307. https://doi.org/10.1029/2005GL022679Pardalos PM, Romeijn HE (eds) (2002) Handbook of global optimization, vols. 1 & 2. Kluwer, DordretchPaul B, De BK, Guha A (2018) Latitudinal variation of F-region ionospheric response during three strongest geomagnetic storms of 2015. Acta Geod Geophys 53:579–606. https://doi.org/10.1007/s40328-018-0221-4Pulinets S, Boyarchuk K (2004) Ionospheric precursors of earthquakes. Springer, BerlinPulinets SA, Legen’ka AD, Gaivoronskaya TV, Depuev VKh (2003) Main phenomenological features of ionospheric precursors of strong earthquakes. J Atmos Sol Terr Phys 65:1337–1347. https://doi.org/10.1016/j.jastp.2003.07.011Reyes J, Morales-Esteban A, Martínez-Álvarez F (2013) Neural networks to predict earthquakes in Chile. Appl Soft Comput 13:1314–1328. https://doi.org/10.1016/j.asoc.2012.10.014Şentürk E, Çepni MS (2018a) A statistical analysis of seismo ionospheric TEC anomalies before 63 Mw ≥ 5.0 earthquakes in Turkey during 2003–2016. Acta Geophys 66:1495–1507. https://doi.org/10.1007/s11600-018-0214-2Şentürk E, Çepni MS (2018b) Ionospheric temporal variations over the region of Turkey: a study based on long-time TEC observations. Acta Geod Geophys 53:623–637. https://doi.org/10.1007/s40328-018-0233-0Şentürk E, Çepni MS (2019) Performance of different weighting and surface fitting techniques on station-wise TEC calculation and modified sine weighting supported by the sun effect. J Spat Sci 64(2):209–220. https://doi.org/10.1080/14498596.2017.1417169Şentürk E, Livaoğlu H, Çepni MS (2019) A comprehensive analysis of ionospheric anomalies before the mw 7.1 Van earthquake on 23 October 2011. J Navig 72(3):702–720. https://doi.org/10.1017/S0373463318000826Shiuly A, Roy N (2018) A generalized VS–N correlation using various regression analysis and genetic algorithm. Acta Geod Geophys 53:479–502. https://doi.org/10.1007/s40328-018-0220-5U.S. Geological Survey (2019) Earthquake catalog. https://earthquake.usgs.gov/earthquakes/search/. Accessed 5 May 2019Warwick JW, Stoker C, Meyer TR (1982) Radio emission associated with rock fracture: possible application to the Great Chilean Earthquake of May 22, 1960. J Geophys Res Solid Earth 87:2851–2859. https://doi.org/10.1029/JB087iB04p02851Yao Y, Chen P, Wu H, Zhang S, Peng W (2012) Analysis of ionospheric anomalies before the 2011 M w 9.0 Japan earthquake. Chin Sci Bull 57(5):500–510. https://doi.org/10.1007/s11434-011-4851-yZakharenkova IE, Shagimuratov II, Krankowski A (2007a) Features of the ionosphere behavior before the Kythira 2006 earthquake. Acta Geophys 55(4):524–534. https://doi.org/10.2478/s11600-007-0031-5Zakharenkova IE, Shagimuratov II, Krankowski A, Lagovsky AF (2007b) Precursory phenomena observed in the total electron content measurements before great Hokkaido earthquake of September 25, 2003 (M = 8.3). Stud Geophys Geod 51(2):267–278. https://doi.org/10.1007/s11200-007-0014-

A novel approach to forecast urban surface-level ozone considering heterogeneous locations and limited information

Surface ozone (O3) is considered an hazard to human health, affecting vegetation crops and ecosystems. Accurate time and location O3 forecasting can help to protect citizens to unhealthy exposures when high levels are expected. Usually, forecasting models use numerous O3 precursors as predictors, limiting the reproducibility of these models to the availability of such information from data providers. This study introduces a 24 h-ahead hourly O3 concentrations forecasting methodology based on bagging and ensemble learning, using just two predictors with lagged O3 concentrations. This methodology was applied on ten-year time series (2006–2015) from three major urban areas of Andalusia (Spain). Its forecasting performance was contrasted with an algorithm especially designed to forecast time series exhibiting temporal patterns. The proposed methodology outperforms the contrast algorithm and yields comparable results to others existing in literature. Its use is encouraged due to its forecasting performance and wide applicability, but also as benchmark methodology.JRC.C.6-Economics of Climate Change, Energy and Transpor