{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":2,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":28,"n_cols":5,"rows_kept":28,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[2,4,2,5],[3,2,3,3],[4,2,4,3],[5,2,5,3],[6,2,6,3],[9,2,9,3],[10,2,10,3],[18,2,18,3],[18,4,18,5],[19,2,19,3],[19,4,19,5],[20,2,20,3],[20,4,20,5],[21,2,21,3],[22,2,22,3],[23,2,23,3],[24,2,24,3],[25,2,25,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNINA9910707383603321"]
[null,null,"Autore",null,"Pilliod David S."]
[null,null,"Titolo",null,"Fuels planning: science synthesis and integration Environmental consequences fact sheet . 15 The Wildlife Habitat Response Model / / David Pilliod"]
[null,null,"Pubbl/distr/stampa",null,"[Fort Collins, Colo.] : , : United States Department of Agriculture, Forest Service, Rocky Mountain Research Station, , 2005"]
[null,null,"Descrizione fisica",null,"1 online resource (2 unnumbered pages) : color illustrations"]
[null,null,"Collana",null,"Research note RMRS ; ; RN-23-15-WWW"]
[null,null,"Soggetti",null,"Forest animals - Effect of forest management on - West (U.S.) - Computer programs"]
[{"$nulls":4},"Forest animals - Habitat - West (U.S.) - Computer programs"]
[{"$nulls":4},"Fire management - Environmental aspects - West (U.S.) - Computer programs"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Title from caption (viewed October 19, 2016)."]
[{"$nulls":4},"\"November 2005.\""]
[{"$nulls":4},"\"Pacific Northwest Research Station.\""]
[{"$nulls":4},"\"North Central Research Station.\""]
[{"$nulls":4},"\"Pacific Southwest Research Station.\""]
[{"$nulls":4},"\"Synthesizing scientific information for fire and fuels project managers.\""]
[null,"2.","Record Nr.",null,"UNINA9910557494303321"]
[null,null,"Autore",null,"Gabaldón Antonio"]
[null,null,"Titolo",null,"Short-Term Load Forecasting 2019"]
[null,null,"Pubbl/distr/stampa",null,"Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021"]
[null,null,"Descrizione fisica",null,"1 online resource (324 p.)"]
[null,null,"Soggetti",null,"History of engineering and technology"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Sommario/riassunto","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."]