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Gay Pride parades are annual arenas of queer public culture, where embodied notions of subjectivity are sold, enacted, transgressed and debated.From Sydney to Rome, Queering Tourism analyses the paradoxes of gay pride parades as tourist events, exploring how the public display of queer bodies - the way they look, what they do, who watches them, and under what regulations - is profoundly important in constructing sexualized subjectivities of bodies and cities. Drawing on extensive collections of interviews, visuals and written media accounts, photographs, advertisement
Gay Pride Day. --- Gay pride parades. --- Gays --- Gays. --- Homosexuality. --- Lesbians --- Lesbians. --- Travel. --- Same-sex attraction --- Sexual orientation --- Bisexuality --- Female gays --- Female homosexuals --- Gay females --- Gay women --- Gayelles --- Gays, Female --- Homosexuals, Female --- Lesbian women --- Sapphists --- Women, Gay --- Women homosexuals --- Women --- Gay people --- Gay persons --- Homosexuals --- Persons --- Gay and Lesbian Pride Day --- Gay Freedom Day --- Gay Liberation Day --- GLBT Pride Day --- Lesbian and Gay Pride Day --- LGBT Pride Day --- Pride Day, Gay --- Gay pride celebrations --- Special days --- Gay pride marches --- LGBT pride parades --- Pride parades, Gay --- Parades --- Gay people. --- Gay men. --- Gay men
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Short-term load forecasting (STLF) plays a key role in the formulation of economic, reliable, and secure operating strategies (planning, scheduling, maintenance, and control processes, among others) for a power system and will be significant in the future. However, there is still much to do in these research areas. The deployment of enabling technologies (e.g., smart meters) has made high-granularity data available for many customer segments and to approach many issues, for instance, to make forecasting tasks feasible at several demand aggregation levels. The first challenge is the improvement of STLF models and their performance at new aggregation levels. Moreover, the mix of renewables in the power system, and the necessity to include more flexibility through demand response initiatives have introduced greater uncertainties, which means new challenges for STLF in a more dynamic power system in the 2030–50 horizon. Many techniques have been proposed and applied for STLF, including traditional statistical models and AI techniques. Besides, distribution planning needs, as well as grid modernization, have initiated the development of hierarchical load forecasting. Analogously, the need to face new sources of uncertainty in the power system is giving more importance to probabilistic load forecasting. This Special Issue deals with both fundamental research and practical application research on STLF methodologies to face the challenges of a more distributed and customer-centered power system.
short-term load forecasting --- demand-side management --- pattern similarity --- hierarchical short-term load forecasting --- feature selection --- weather station selection --- load forecasting --- special days --- regressive models --- electric load forecasting --- data preprocessing technique --- multiobjective optimization algorithm --- combined model --- Nordic electricity market --- electricity demand --- component estimation method --- univariate and multivariate time series analysis --- modeling and forecasting --- deep learning --- wavenet --- long short-term memory --- demand response --- hybrid energy system --- data augmentation --- convolution neural network --- residential load forecasting --- forecasting --- time series --- cubic splines --- real-time electricity load --- seasonal patterns --- Load forecasting --- VSTLF --- bus load forecasting --- DBN --- PSR --- distributed energy resources --- prosumers --- building electric energy consumption forecasting --- cold-start problem --- transfer learning --- multivariate random forests --- random forest --- electricity consumption --- lasso --- Tikhonov regularization --- load metering --- preliminary load --- short term load forecasting --- performance criteria --- power systems --- cost analysis --- day ahead --- feature extraction --- deep residual neural network --- multiple sources --- electricity
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Governors --- Environmental protection --- Environmentalism --- Earth Day --- Conservationists --- Environmentalists --- Legislators --- Kings and rulers --- Public officers --- Environmental quality management --- Protection of environment --- Environmental sciences --- Applied ecology --- Environmental engineering --- Environmental policy --- Environmental quality --- Earth Day, 1970 --- Earth Day, 1990 --- Special days --- History --- History. --- Nelson, Gaylord, --- United States. --- Mei-kuo tsʻan i yüan --- United States --- ABŞ --- ABSh --- Ameerika Ühendriigid --- America (Republic) --- Amerika Birlăshmish Shtatlary --- Amerika Birlăşmi Ştatları --- Amerika Birlăşmiş Ştatları --- Amerika ka Kelenyalen Jamanaw --- Amerika Qūrama Shtattary --- Amerika Qŭshma Shtatlari --- Amerika Qushma Shtattary --- Amerika (Republic) --- Amerikai Egyesült Államok --- Amerikanʹ Veĭtʹsėndi︠a︡vks Shtattnė --- Amerikări Pĕrleshu̇llĕ Shtatsem --- Amerikas Forenede Stater --- Amerikayi Miatsʻyal Nahangner --- Ameriketako Estatu Batuak --- Amirika Carékat --- AQSh --- Ar. ha-B. --- Arhab --- Artsot ha-Berit --- Artzois Ha'bris --- Bí-kok --- Ē.P.A. --- EE.UU. --- Egyesült Államok --- ĒPA --- Estados Unidos --- Estados Unidos da América do Norte --- Estados Unidos de América --- Estaos Xuníos --- Estaos Xuníos d'América --- Estatos Unitos --- Estatos Unitos d'America --- Estats Units d'Amèrica --- Ètats-Unis d'Amèrica --- États-Unis d'Amérique --- Fareyniḳṭe Shṭaṭn --- Feriene Steaten --- Feriene Steaten fan Amearika --- Forente stater --- FS --- Hēnomenai Politeiai Amerikēs --- Hēnōmenes Politeies tēs Amerikēs --- Hiwsisayin Amerikayi Miatsʻeal Tērutʻiwnkʻ --- Istadus Unidus --- Jungtinės Amerikos valstybės --- Mei guo --- Mei-kuo --- Meiguo --- Mî-koet --- Miatsʻyal Nahangner --- Miguk --- Na Stàitean Aonaichte --- NSA --- S.U.A. --- SAD --- Saharat ʻAmērikā --- SASht --- Severo-Amerikanskie Shtaty --- Severo-Amerikanskie Soedinennye Shtaty --- Si︠e︡vero-Amerikanskīe Soedinennye Shtaty --- Sjedinjene Američke Države --- Soedinennye Shtaty Ameriki --- Soedinennye Shtaty Severnoĭ Ameriki --- Soedinennye Shtaty Si︠e︡vernoĭ Ameriki --- Spojené staty americké --- SShA --- Stadoù-Unanet Amerika --- Stáit Aontaithe Mheiriceá --- Stany Zjednoczone --- Stati Uniti --- Stati Uniti d'America --- Stâts Unîts --- Stâts Unîts di Americhe --- Steatyn Unnaneysit --- Steatyn Unnaneysit America --- SUA (Stati Uniti d'America) --- Sŭedineni amerikanski shtati --- Sŭedinenite shtati --- Tetã peteĩ reko Amérikagua --- U.S. --- U.S.A. --- United States of America --- Unol Daleithiau --- Unol Daleithiau America --- Unuiĝintaj Ŝtatoj de Ameriko --- US --- USA --- Usono --- Vaeinigte Staatn --- Vaeinigte Staatn vo Amerika --- Vereinigte Staaten --- Vereinigte Staaten von Amerika --- Verenigde State van Amerika --- Verenigde Staten --- VS --- VSA --- Wááshindoon Bikéyah Ałhidadiidzooígíí --- Wilāyāt al-Muttaḥidah --- Wilāyāt al-Muttaḥidah al-Amirīkīyah --- Wilāyāt al-Muttaḥidah al-Amrīkīyah --- Yhdysvallat --- Yunaeted Stet --- Yunaeted Stet blong Amerika --- ZDA --- Združene države Amerike --- Zʹi︠e︡dnani Derz︠h︡avy Ameryky --- Zjadnośone staty Ameriki --- Zluchanyi︠a︡ Shtaty Ameryki --- Zlucheni Derz︠h︡avy --- ZSA --- Η.Π.Α. --- Ηνωμένες Πολιτείες της Αμερικής --- Америка (Republic) --- Американь Вейтьсэндявкс Штаттнэ --- Америкӑри Пӗрлешӳллӗ Штатсем --- САЩ --- Съединените щати --- Злучаныя Штаты Амерыкі --- ولايات المتحدة --- ولايات المتّحدة الأمريكيّة --- ولايات المتحدة الامريكية --- 미국 --- Environmental conditions. --- Spojené obce severoamerické --- États-Unis --- É.-U. --- ÉU
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