{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T07:55:17Z","timestamp":1743062117707,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":26,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811063633"},{"type":"electronic","value":"9789811063640"}],"license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017]]},"DOI":"10.1007\/978-981-10-6364-0_55","type":"book-chapter","created":{"date-parts":[[2017,8,24]],"date-time":"2017-08-24T01:22:33Z","timestamp":1503537753000},"page":"547-555","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["The Role of Intelligent Computing in Load Forecasting for Distributed Energy System"],"prefix":"10.1007","author":[{"given":"Pengwei","family":"Su","sequence":"first","affiliation":[]},{"given":"Yan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Jun","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Shuai","family":"Deng","sequence":"additional","affiliation":[]},{"given":"Ligai","family":"Kang","sequence":"additional","affiliation":[]},{"given":"Zelin","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yu","family":"Jin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2017,8,25]]},"reference":[{"issue":"1","key":"55_CR1","first-page":"147","volume":"39","author":"L Nihuan","year":"2011","unstructured":"Nihuan, L.: Review of the short-term load forecasting methods of electric power system. Power Syst. Protect. Control. 39(1), 147\u2013152 (2011). (in Chinese)","journal-title":"Power Syst. Protect. Control."},{"key":"55_CR2","volume-title":"MATLAB data analysis and data mining","author":"Z Liangjun","year":"2015","unstructured":"Liangjun, Z., Tan, Y., Gang, X.: MATLAB data analysis and data mining. China Machine Press, Beijing (2015). (in Chinese)"},{"key":"55_CR3","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1016\/j.renene.2016.01.084","volume":"91","author":"A Molin","year":"2016","unstructured":"Molin, A., Schneider, S., Rohdin, P., et al.: Assessing a regional building applied PV potential\u2014spatial and dynamic analysis of supply and load matching. Renew. Energy 91, 261\u2013274 (2016)","journal-title":"Renew. Energy"},{"key":"55_CR4","doi-asserted-by":"publisher","first-page":"1330","DOI":"10.1016\/j.jclepro.2016.07.173","volume":"137","author":"S V\u00e4is\u00e4nen","year":"2016","unstructured":"V\u00e4is\u00e4nen, S., Mikkil\u00e4, M., Havukainen, J., et al.: Using a multi-method approach for decision-making about a sustainable local distributed energy system: a case study from Finland. J. Clean. Prod. 137, 1330\u20131338 (2016)","journal-title":"J. Clean. Prod."},{"key":"55_CR5","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1016\/j.apenergy.2015.04.116","volume":"154","author":"F Guarino","year":"2015","unstructured":"Guarino, F., Cassar\u00e0, P., Longo, S., et al.: Load match optimization of a residential building case study: a cross-entropy based electricity storage sizing algorithm. Appl. Energy 154, 380\u2013391 (2015)","journal-title":"Appl. Energy"},{"key":"55_CR6","doi-asserted-by":"crossref","unstructured":"Singh, A.K., Ibraheem, I., Khatoon, S., et al.: Load forecasting techniques and methodologies: a review. In: International Conference on Power, Control and Embedded Systems, pp. 1\u201310 (2012)","DOI":"10.1109\/ICPCES.2012.6508132"},{"issue":"5","key":"55_CR7","doi-asserted-by":"publisher","first-page":"877","DOI":"10.1016\/j.energy.2014.07.064","volume":"74","author":"KM Powell","year":"2014","unstructured":"Powell, K.M., Sriprasad, A., Cole, W.J., et al.: Heating, cooling, and electrical load forecasting for a large-scale district energy system. Energy 74(5), 877\u2013885 (2014)","journal-title":"Energy"},{"key":"55_CR8","doi-asserted-by":"crossref","unstructured":"Gupta, S., Singh, V., Mittal, A.P., et al.: Weekly load prediction using wavelet neural network approach. In: Second International Conference on Computational Intelligence and Communication Technology, pp. 174\u2013179 (2016)","DOI":"10.1109\/CICT.2016.42"},{"key":"55_CR9","doi-asserted-by":"publisher","first-page":"478","DOI":"10.1016\/j.enbuild.2016.09.068","volume":"133","author":"S Idowu","year":"2016","unstructured":"Idowu, S., Saguna, S., \u00c5hlund, C., et al.: Applied machine learning: forecasting heat load in district heating system. Energy Build. 133, 478\u2013488 (2016)","journal-title":"Energy Build."},{"key":"55_CR10","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1016\/j.enbuild.2015.12.050","volume":"121","author":"C Deb","year":"2016","unstructured":"Deb, C., Eang, L.S., Yang, J., et al.: Forecasting diurnal cooling energy load for institutional buildings using artificial neural networks. Energy Build. 121, 284\u2013297 (2016)","journal-title":"Energy Build."},{"key":"55_CR11","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.enbuild.2015.04.011","volume":"99","author":"H Chitsaz","year":"2015","unstructured":"Chitsaz, H., Shaker, H., Zareipour, H., et al.: Short-term electricity load forecasting of buildings in micro grids. Energy Build. 99, 50\u201360 (2015)","journal-title":"Energy Build."},{"issue":"3","key":"55_CR12","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1016\/j.energy.2015.04.109","volume":"87","author":"M Proti\u0107","year":"2015","unstructured":"Proti\u0107, M., Shahaboddin, S., et al.: Forecasting of consumers heat load in district heating systems using the support vector machine with a discrete wavelet transform algorithm. Energy 87(3), 343\u2013351 (2015)","journal-title":"Energy"},{"key":"55_CR13","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.knosys.2014.12.008","volume":"76","author":"A Abdoos","year":"2015","unstructured":"Abdoos, A., Hemmati, M., Abdoos, A.A.: Short term load forecasting using a hybrid intelligent method. Knowl. Based Syst. 76, 139\u2013147 (2015)","journal-title":"Knowl. Based Syst."},{"key":"55_CR14","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1016\/j.enbuild.2014.07.036","volume":"82","author":"JS Chou","year":"2014","unstructured":"Chou, J.S., Bui, D.K.: Modeling heating and cooling loads by artificial intelligence for energy-efficient building design. Energy Build. 82, 437\u2013446 (2014)","journal-title":"Energy Build."},{"key":"55_CR15","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1016\/j.egypro.2014.12.383","volume":"62","author":"F Rodrigues","year":"2014","unstructured":"Rodrigues, F., Cardeira, C., Calado, J.M.F., et al.: The daily and hourly energy consumption and load forecasting using artificial neural network method: a case study using a set of 93 households in Portugal. Energy Procedia 62, 220\u2013229 (2014)","journal-title":"Energy Procedia"},{"key":"55_CR16","doi-asserted-by":"crossref","unstructured":"Xue, B., Geng, J., Zheng, Y., et al.: Application of genetic algorithm to middle-long term optimal combination power load forecast. In: IEEE Region 10 Annual International Conference, pp. 1\u20134 (2013)","DOI":"10.1109\/TENCON.2013.6719074"},{"key":"55_CR17","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.apenergy.2016.02.114","volume":"170","author":"S Li","year":"2016","unstructured":"Li, S., Goel, L., Wang, P.: An ensemble approach for short-term load forecasting by extreme learning machine. Appl. Energy 170, 22\u201329 (2016)","journal-title":"Appl. Energy"},{"key":"55_CR18","doi-asserted-by":"publisher","first-page":"544","DOI":"10.1016\/j.apenergy.2016.06.133","volume":"179","author":"T Fang","year":"2016","unstructured":"Fang, T., Lahdelma, R.: Evaluation of a multiple linear regression model and SARIMA model in forecasting heat demand for district heating system. Appl. Energy 179, 544\u2013552 (2016)","journal-title":"Appl. Energy"},{"key":"55_CR19","doi-asserted-by":"crossref","unstructured":"Papakonstantinou, N., Savolainen, J., Koistinen, J., et al.: District heating temperature control algorithm based on short term weather forecast and consumption predictions. In: IEEE International Conference on Emerging Technologies and Factory Automation, ETFA (2016)","DOI":"10.1109\/ETFA.2016.7733748"},{"key":"55_CR20","doi-asserted-by":"publisher","first-page":"1057","DOI":"10.1016\/j.ijepes.2014.08.006","volume":"64","author":"WJ Lee","year":"2015","unstructured":"Lee, W.J., Hong, J.: A hybrid dynamic and fuzzy time series model for mid-term power load forecasting. Int. J. Electr. Power Energy Syst. 64, 1057\u20131062 (2015)","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"55_CR21","doi-asserted-by":"publisher","first-page":"1040","DOI":"10.1016\/j.enconman.2015.07.041","volume":"103","author":"A Lahouar","year":"2015","unstructured":"Lahouar, A., Slama, J.B.H.: Day-ahead load forecast using random forest and expert input selection. Energy Conv. Manag. 103, 1040\u20131051 (2015)","journal-title":"Energy Conv. Manag."},{"key":"55_CR22","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.ijepes.2015.03.003","volume":"73","author":"CW Lou","year":"2015","unstructured":"Lou, C.W., Dong, M.C.: A novel random fuzzy neural networks for tackling uncertainties of electric load forecasting. Int. J. Electr. Power Energy Syst. 73, 34\u201344 (2015)","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"55_CR23","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1016\/j.apenergy.2014.09.004","volume":"136","author":"A Vaghefi","year":"2014","unstructured":"Vaghefi, A., Jafari, M.A., Bisse, E., et al.: Modeling and forecasting of cooling and electricity load demand. Appl. Energy 136, 186\u2013196 (2014)","journal-title":"Appl. Energy"},{"issue":"4","key":"55_CR24","first-page":"75","volume":"28","author":"W Qiao","year":"2015","unstructured":"Qiao, W., Chen, B.: Hourly load prediction for natural gas based on Haar wavelet tansforming and ARIMA-RBF. Shiyou Huagong Gaodeng Xuexiao Xuebao\/J. Petrochem. Univ. 28(4), 75\u201380 (2015)","journal-title":"Shiyou Huagong Gaodeng Xuexiao Xuebao\/J. Petrochem. Univ."},{"key":"55_CR25","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1016\/j.epsr.2016.06.003","volume":"140","author":"MH Amini","year":"2016","unstructured":"Amini, M.H., Kargarian, A., Karabasoglu, O.: ARIMA-based decoupled time series forecasting of electric vehicle charging demand for stochastic power system operation. Electric Power Syst. Res. 140, 378\u2013390 (2016)","journal-title":"Electric Power Syst. Res."},{"issue":"08","key":"55_CR26","first-page":"35","volume":"35","author":"Y Chen","year":"2011","unstructured":"Chen, Y., Zhang, B., Wang, J.: Active control strategy for microgrid energy storage system based on short-term load forecasting. Power Syst. Technol. 35(08), 35\u201340 (2011). (in Chinese)","journal-title":"Power Syst. Technol."}],"container-title":["Communications in Computer and Information Science","Advanced Computational Methods in Energy, Power, Electric Vehicles, and Their Integration"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-10-6364-0_55","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T17:56:37Z","timestamp":1710352597000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-10-6364-0_55"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"ISBN":["9789811063633","9789811063640"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-981-10-6364-0_55","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2017]]},"assertion":[{"value":"25 August 2017","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICSEE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing for Sustainable Energy and Environment","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nanjing","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2017","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2017","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 September 2017","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icsee2017","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.lsms-icsee.shu.edu.cn\/2017\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}