Day-ahead forecasting of residential electric power consumption for energy management using Long Short-Term Memory encoder–decoder model
Energy management in smart buildings and energy communities needs short-term load demand forecasting for optimization-based scheduling, dispatch, and real-time operation. However, producing accurate forecasting for individual residential households is more challenging compared to the forecasting of...
Uloženo v:
| Vydáno v: | Mathematics and computers in simulation Ročník 224; s. 63 - 75 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier B.V
01.10.2024
|
| Témata: | |
| ISSN: | 0378-4754, 1872-7166 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Energy management in smart buildings and energy communities needs short-term load demand forecasting for optimization-based scheduling, dispatch, and real-time operation. However, producing accurate forecasting for individual residential households is more challenging compared to the forecasting of load demand at the distribution level, which is smoother and benefits from statistical compensation of errors. This paper presents a day-ahead forecasting technique for individual residential load demand that is based on the Long Short-Term Memory encoder–decoder architecture, which is extended to consider possibly differing sets of past and future exogenous variables. A novel focus is posed on the validation of the proposed approach considering that it is tailored for use by energy management systems. A publicly available dataset was used for validation, and the approach was compared with three other methods, resulting in a reduction of the Mean Absolute Scaled Error by up to 8%. |
|---|---|
| AbstractList | Energy management in smart buildings and energy communities needs short-term load demand forecasting for optimization-based scheduling, dispatch, and real-time operation. However, producing accurate forecasting for individual residential households is more challenging compared to the forecasting of load demand at the distribution level, which is smoother and benefits from statistical compensation of errors. This paper presents a day-ahead forecasting technique for individual residential load demand that is based on the Long Short-Term Memory encoder–decoder architecture, which is extended to consider possibly differing sets of past and future exogenous variables. A novel focus is posed on the validation of the proposed approach considering that it is tailored for use by energy management systems. A publicly available dataset was used for validation, and the approach was compared with three other methods, resulting in a reduction of the Mean Absolute Scaled Error by up to 8%. |
| Author | La Tona, G. Di Piazza, M.C. Luna, M. |
| Author_xml | – sequence: 1 givenname: G. orcidid: 0000-0002-9097-6626 surname: La Tona fullname: La Tona, G. email: giuseppe.latona@cnr.it – sequence: 2 givenname: M. orcidid: 0000-0001-8900-9367 surname: Luna fullname: Luna, M. – sequence: 3 givenname: M.C. surname: Di Piazza fullname: Di Piazza, M.C. |
| BookMark | eNqFkLtOJDEQRS0EEsPjDwj8A91r99MQrLTiLQ0iAGKr2q4ePGq3R7YBTUZMyh_yJbgZIgI2qaqgzpXu2SPboxuRkCPOcs5482eZW4jK2bxgRZmzJme83SIzLtoia3nTbJMZK1uRVW1d7ZK9EJaMsXTXM_J2BusMHhE07Z1HBSGacUFdTz0Go3GMBgaKA6rojaIr94KeKjeGJ7uKxo0TRXFEv1hTCyMs0CaGPoUpZe7SuHt0Pmb36C29Qev8Or0rp9F_vL5r_LqoTXM4IDs9DAEPv_c-ebg4vz-9yua3l9en_-aZKusiZlprVYEQmtW6groGPBadOC77VFdhxzkrQHWdFqKuylarhiNvhda8Fh2UAsp9crLJVd6F4LGXykSYykQPZpCcycmqXMqNVTlZlayRyWqCqx_wyhsLfv0_7O8Gw1Ts2aCXQZnkAbVJ0qPUzvwe8AmelZuJ |
| CitedBy_id | crossref_primary_10_1016_j_enbuild_2025_115951 crossref_primary_10_1016_j_matcom_2025_04_044 crossref_primary_10_1016_j_apenergy_2024_122722 crossref_primary_10_1016_j_aei_2025_103754 crossref_primary_10_1109_ACCESS_2024_3521010 |
| Cites_doi | 10.1109/ACCESS.2019.2963045 10.1109/INDIN.2018.8471953 10.1109/TSG.2017.2686012 10.1109/TNNLS.2016.2582924 10.1109/IECON.2016.7793413 10.1109/ICIOT.2019.00029 10.1016/j.ijforecast.2006.03.001 10.1016/j.segan.2016.02.005 10.3390/en14061598 10.3390/app9102120 10.3390/app9204237 10.1109/ICSCAN53069.2021.9526485 10.1016/j.egypro.2017.12.423 10.1109/TSG.2017.2753802 10.1016/j.matcom.2020.05.010 10.1109/ACCESS.2020.3009537 10.1016/j.ijforecast.2015.11.011 10.1109/PTC.2019.8810899 10.1109/IECON.2019.8926801 10.1016/j.energy.2019.05.230 10.1109/EAIT.2018.8470406 10.1109/ACCESS.2020.3028281 10.1109/EEEIC/ICPSEurope49358.2020.9160650 10.1016/j.apenergy.2017.12.051 10.1016/j.solener.2012.03.006 10.1109/ISIE.2017.8001465 10.1016/j.ijforecast.2019.04.014 10.1016/j.ijepes.2021.107023 |
| ContentType | Journal Article |
| Copyright | 2023 The Authors |
| Copyright_xml | – notice: 2023 The Authors |
| DBID | 6I. AAFTH AAYXX CITATION |
| DOI | 10.1016/j.matcom.2023.06.017 |
| DatabaseName | ScienceDirect Open Access Titles Elsevier:ScienceDirect:Open Access CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1872-7166 |
| EndPage | 75 |
| ExternalDocumentID | 10_1016_j_matcom_2023_06_017 S0378475423002720 |
| GroupedDBID | --K --M -~X .~1 0R~ 1B1 1RT 1~. 1~5 29M 4.4 457 4G. 5GY 5VS 63O 6I. 7-5 71M 8P~ 9JN 9JO AAAKF AAAKG AACTN AAEDT AAEDW AAFTH AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AARIN AAXUO ABAOU ABEFU ABFNM ABJNI ABMAC ABUCO ABXDB ACDAQ ACGFS ACNNM ACRLP ADBBV ADEZE ADGUI ADMUD ADTZH AEBSH AECPX AEKER AENEX AFFNX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AIEXJ AIGVJ AIKHN AITUG AJOXV AKRWK ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ APLSM ARUGR AXJTR AZFZN BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA HAMUX HLZ HMJ HVGLF HZ~ H~9 IHE J1W JJJVA KOM LG9 M26 M41 MHUIS MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG RNS ROL RPZ SBC SDF SDG SES SEW SME SPC SPCBC SSB SSD SST SSW SSZ T5K TN5 WUQ XPP ZMT ~02 ~G- 9DU AATTM AAXKI AAYWO AAYXX ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKYEP ANKPU APXCP CITATION EFKBS EFLBG ~HD |
| ID | FETCH-LOGICAL-c352t-dddc4a88d05d4a55ae98b893f187ceb1102acbbd885437dc61e178dd158ba38a3 |
| ISICitedReferencesCount | 8 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001322504500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0378-4754 |
| IngestDate | Sat Nov 29 05:44:56 EST 2025 Tue Nov 18 22:26:24 EST 2025 Sat Jun 29 15:30:58 EDT 2024 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Residential electrical consumption LSTM Forecasting Energy management |
| Language | English |
| License | This is an open access article under the CC BY license. |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c352t-dddc4a88d05d4a55ae98b893f187ceb1102acbbd885437dc61e178dd158ba38a3 |
| ORCID | 0000-0001-8900-9367 0000-0002-9097-6626 |
| OpenAccessLink | https://dx.doi.org/10.1016/j.matcom.2023.06.017 |
| PageCount | 13 |
| ParticipantIDs | crossref_citationtrail_10_1016_j_matcom_2023_06_017 crossref_primary_10_1016_j_matcom_2023_06_017 elsevier_sciencedirect_doi_10_1016_j_matcom_2023_06_017 |
| PublicationCentury | 2000 |
| PublicationDate | October 2024 2024-10-00 |
| PublicationDateYYYYMMDD | 2024-10-01 |
| PublicationDate_xml | – month: 10 year: 2024 text: October 2024 |
| PublicationDecade | 2020 |
| PublicationTitle | Mathematics and computers in simulation |
| PublicationYear | 2024 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | A. Vaswani, et al., Attention is all you need, in: Advances in neural information processing systems, 2017, pp. 5998–6008. L. Sehovac, C. Nesen, K. Grolinger, Forecasting Building Energy Consumption with Deep Learning: A Sequence to Sequence Approach, in: 2019 IEEE International Congress on Internet of Things (ICIOT), 2019, pp. 108–116 Shi, Xu, Li (b32) 2018; 9 LeCun, Bottou, Orr, Müller, BackProp (b22) 2012; vol. 7700 Oreshkin, Carpov, Chapados, Bengio (b27) 2019 Makridakis, Spiliotis, Assimakopoulos, Competition (b23) 2020; 36 Shi, Xu, Ma, Zhang, Li, Li (b33) 2017; 142 O’Malley (b26) 2019 Khan, Haq, Khan, Rho, Lee, Baik (b14) 2021; 133 Kong, Dong, Jia, Hill, Xu, Zhang (b16) 2019; 10 Hebrail, Berards (b8) 2012 Chollet (b4) 2015 Hong, Fan (b10) 2016; 32 Rahman, Srikumar, Smith (b28) 2018; 212 La Tona, Luna, Di Piazza, Di Piazza (b20) 2019; 9 Hyndman, Athanasopoulos (b12) 2021 S. Kumar, L. Hussain, S. Banarjee, M. Reza, Energy Load Forecasting using Deep Learning Approach-LSTM and GRU in Spark Cluster, in: 2018 Fifth International Conference on Emerging Applications of Information Technology (EAIT), 2018, pp. 1–4 Hinton, Srivastava, Krizhevsky, Sutskever, Salakhutdinov (b9) 2012 D.L. Marino, K. Amarasinghe, M. Manic, Building energy load forecasting using Deep Neural Networks, in: IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society, 2016, pp. 7046–7051 Huld, Müller, Gambardella (b11) 2012; 86 G. La Tona, M. Luna, A. Di Piazza, M.C. Di Piazza, Development of a Forecasting Module based on Tensorflow for Use in Energy Management Systems, in: IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society, 2019, pp. 3063–3068 Kim, Cho (b15) 2019; 182 Di Piazza, Di Piazza, La Tona, Luna (b6) 2021; 184 Sajjad others (b30) 2020; 8 Sutskever, Vinyals, Le (b35) 2014; 4 Hyndman, Koehler (b13) 2006; 22 M. Suresh, M.S. Anbarasi, J. Divyabharathi, D. Harshavardeni, S. Meena, Household Electricity Power Consumption Prediction Using CNN-GRU Techniques, in: 2021 International Conference on System Computation, Automation and Networking, ICSCAN, 2021 H. Wilms, M. Cupelli, A. Monti, Combining auto-regression with exogenous variables in sequence-to-sequence recurrent neural networks for short-term load forecasting, in: 2018 IEEE 16th International Conference on Industrial Informatics (INDIN), 2018, pp. 673–679 . Ullah, Ullah, Haq, Rho, Baik (b36) 2020; 8 Le, Vo, Vo, Hwang, Rho, Baik (b21) 2019; 9 K. Amarasinghe, D.L. Marino, M. Manic, Deep neural networks for energy load forecasting, in: 2017 IEEE 26th International Symposium on Industrial Electronics (ISIE), 2017, pp. 1483–1488 Greff, Srivastava, Koutnik, Steunebrink, Schmidhuber (b7) 2017; 28 R. Rajabi, A. Estebsari, Deep learning based forecasting of individual residential loads using recurrence plots, in: 2019 IEEE Milan PowerTech PowerTech 2019, 2019 Alhussein, Aurangzeb, Haider (b2) 2020; 8 Mocanu, Nguyen, Gibescu, Kling (b25) 2016; 6 Abadi (b1) 2015 La Tona, Di Piazza, Luna (b18) 2021; 14 G. Di Lorenzo, L. Martirano, R. Araneo, G. Petrone, Modeling and Design of a Residential Energy Community with PV Sharing, in: Proceedings - 2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe, EEEIC/ I and CPS Europe 2020, 2020 10.1016/j.matcom.2023.06.017_b5 Ullah (10.1016/j.matcom.2023.06.017_b36) 2020; 8 La Tona (10.1016/j.matcom.2023.06.017_b18) 2021; 14 10.1016/j.matcom.2023.06.017_b3 Shi (10.1016/j.matcom.2023.06.017_b32) 2018; 9 Alhussein (10.1016/j.matcom.2023.06.017_b2) 2020; 8 Rahman (10.1016/j.matcom.2023.06.017_b28) 2018; 212 Hinton (10.1016/j.matcom.2023.06.017_b9) 2012 Chollet (10.1016/j.matcom.2023.06.017_b4) 2015 Huld (10.1016/j.matcom.2023.06.017_b11) 2012; 86 Kim (10.1016/j.matcom.2023.06.017_b15) 2019; 182 Mocanu (10.1016/j.matcom.2023.06.017_b25) 2016; 6 10.1016/j.matcom.2023.06.017_b31 Di Piazza (10.1016/j.matcom.2023.06.017_b6) 2021; 184 Khan (10.1016/j.matcom.2023.06.017_b14) 2021; 133 10.1016/j.matcom.2023.06.017_b34 10.1016/j.matcom.2023.06.017_b37 10.1016/j.matcom.2023.06.017_b17 Makridakis (10.1016/j.matcom.2023.06.017_b23) 2020; 36 LeCun (10.1016/j.matcom.2023.06.017_b22) 2012; vol. 7700 10.1016/j.matcom.2023.06.017_b38 10.1016/j.matcom.2023.06.017_b19 Sutskever (10.1016/j.matcom.2023.06.017_b35) 2014; 4 La Tona (10.1016/j.matcom.2023.06.017_b20) 2019; 9 Shi (10.1016/j.matcom.2023.06.017_b33) 2017; 142 Hyndman (10.1016/j.matcom.2023.06.017_b12) 2021 Greff (10.1016/j.matcom.2023.06.017_b7) 2017; 28 Hong (10.1016/j.matcom.2023.06.017_b10) 2016; 32 Kong (10.1016/j.matcom.2023.06.017_b16) 2019; 10 O’Malley (10.1016/j.matcom.2023.06.017_b26) 2019 Oreshkin (10.1016/j.matcom.2023.06.017_b27) 2019 Sajjad others (10.1016/j.matcom.2023.06.017_b30) 2020; 8 Le (10.1016/j.matcom.2023.06.017_b21) 2019; 9 Hyndman (10.1016/j.matcom.2023.06.017_b13) 2006; 22 Abadi (10.1016/j.matcom.2023.06.017_b1) 2015 10.1016/j.matcom.2023.06.017_b24 10.1016/j.matcom.2023.06.017_b29 Hebrail (10.1016/j.matcom.2023.06.017_b8) 2012 |
| References_xml | – volume: 32 start-page: 914 year: 2016 end-page: 938 ident: b10 article-title: Probabilistic electric load forecasting: A tutorial review publication-title: Int. J. Forecast. – volume: 28 start-page: 2222 year: 2017 end-page: 2232 ident: b7 article-title: LSTM: A search space odyssey publication-title: IEEE Trans. Neural Netw. Learn. Syst. – reference: S. Kumar, L. Hussain, S. Banarjee, M. Reza, Energy Load Forecasting using Deep Learning Approach-LSTM and GRU in Spark Cluster, in: 2018 Fifth International Conference on Emerging Applications of Information Technology (EAIT), 2018, pp. 1–4, – year: 2021 ident: b12 article-title: Forecasting: principles and practice – year: 2012 ident: b8 article-title: Individual household electric power consumption data set. UCI machine learning repository – volume: 8 start-page: 143759 year: 2020 end-page: 143768 ident: b30 article-title: A novel CNN-GRU-based hybrid approach for short-term residential load forecasting publication-title: IEEE Access – reference: M. Suresh, M.S. Anbarasi, J. Divyabharathi, D. Harshavardeni, S. Meena, Household Electricity Power Consumption Prediction Using CNN-GRU Techniques, in: 2021 International Conference on System Computation, Automation and Networking, ICSCAN, 2021, – volume: 184 start-page: 294 year: 2021 end-page: 305 ident: b6 article-title: An artificial neural network-based forecasting model of energy-related time series for electrical grid management publication-title: Math. Comput. Simulation – start-page: 1 year: 2012 end-page: 18 ident: b9 article-title: Improving neural networks by preventing co-adaptation of feature detectors – volume: 36 start-page: 54 year: 2020 end-page: 74 ident: b23 article-title: 100000 Time series and 61 forecasting methods publication-title: Int. J. Forecasting – volume: 182 start-page: 72 year: 2019 end-page: 81 ident: b15 article-title: Predicting residential energy consumption using CNN-LSTM neural networks publication-title: Energy – volume: 22 start-page: 679 year: 2006 end-page: 688 ident: b13 article-title: Another look at measures of forecast accuracy publication-title: Int. J. Forecast. – reference: D.L. Marino, K. Amarasinghe, M. Manic, Building energy load forecasting using Deep Neural Networks, in: IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society, 2016, pp. 7046–7051, – volume: 8 start-page: 123369 year: 2020 end-page: 123380 ident: b36 article-title: Short-term prediction of residential power energy consumption via CNN and multi-layer bi-directional LSTM networks publication-title: IEEE Access – reference: K. Amarasinghe, D.L. Marino, M. Manic, Deep neural networks for energy load forecasting, in: 2017 IEEE 26th International Symposium on Industrial Electronics (ISIE), 2017, pp. 1483–1488, – volume: 8 start-page: 180544 year: 2020 end-page: 180557 ident: b2 article-title: Hybrid CNN-LSTM model for short-term individual household load forecasting publication-title: IEEE Access – year: 2019 ident: b26 article-title: “Kerastuner.” – reference: L. Sehovac, C. Nesen, K. Grolinger, Forecasting Building Energy Consumption with Deep Learning: A Sequence to Sequence Approach, in: 2019 IEEE International Congress on Internet of Things (ICIOT), 2019, pp. 108–116, – volume: 4 start-page: 3104 year: 2014 end-page: 3112 ident: b35 article-title: Sequence to sequence learning with neural networks publication-title: Adv. Neural Inform. Process. Syst. – volume: 86 start-page: 1803 year: 2012 end-page: 1815 ident: b11 article-title: A new solar radiation database for estimating PV performance in Europe and Africa publication-title: Sol. Energy – reference: G. Di Lorenzo, L. Martirano, R. Araneo, G. Petrone, Modeling and Design of a Residential Energy Community with PV Sharing, in: Proceedings - 2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe, EEEIC/ I and CPS Europe 2020, 2020, – volume: 9 start-page: 5271 year: 2018 end-page: 5280 ident: b32 article-title: Deep learning for household load forecasting—A novel pooling deep RNN publication-title: IEEE Trans. Smart Grid – volume: 133 year: 2021 ident: b14 article-title: DB-Net: A novel dilated CNN based multi-step forecasting model for power consumption in integrated local energy systems publication-title: Int. J. Electr. Power Energy Syst. – volume: vol. 7700 start-page: 9 year: 2012 end-page: 48 ident: b22 article-title: Efficient backprop publication-title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics – volume: 6 start-page: 91 year: 2016 end-page: 99 ident: b25 article-title: Deep learning for estimating building energy consumption publication-title: Sustain. Energy Grids Netw. – reference: R. Rajabi, A. Estebsari, Deep learning based forecasting of individual residential loads using recurrence plots, in: 2019 IEEE Milan PowerTech PowerTech 2019, 2019, – reference: . – reference: G. La Tona, M. Luna, A. Di Piazza, M.C. Di Piazza, Development of a Forecasting Module based on Tensorflow for Use in Energy Management Systems, in: IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society, 2019, pp. 3063–3068, – year: 2015 ident: b4 article-title: Keras – volume: 9 start-page: 4237 year: 2019 ident: b21 article-title: Improving electric energy consumption prediction using CNN and bi-LSTM publication-title: Appl. Sci. – reference: A. Vaswani, et al., Attention is all you need, in: Advances in neural information processing systems, 2017, pp. 5998–6008. – year: 2015 ident: b1 article-title: TensorFlow: large-scale machine learning on heterogeneous systems – volume: 142 start-page: 2791 year: 2017 end-page: 2796 ident: b33 article-title: A whole system assessment of novel deep learning approach on short-term load forecasting publication-title: Energy Procedia – reference: H. Wilms, M. Cupelli, A. Monti, Combining auto-regression with exogenous variables in sequence-to-sequence recurrent neural networks for short-term load forecasting, in: 2018 IEEE 16th International Conference on Industrial Informatics (INDIN), 2018, pp. 673–679, – start-page: 1 year: 2019 end-page: 31 ident: b27 article-title: N-BEATS: Neural basis expansion analysis for interpretable time series forecasting – volume: 14 start-page: 1598 year: 2021 ident: b18 article-title: Effect of daily forecasting frequency on rolling-horizon-based EMS reducing electrical demand uncertainty in microgrids publication-title: Energies – volume: 9 start-page: 2120 year: 2019 ident: b20 article-title: Towards the real-world deployment of a smart home EMS: A DP implementation on the raspberry Pi publication-title: Appl. Sci. – volume: 10 start-page: 841 year: 2019 end-page: 851 ident: b16 article-title: Short-term residential load forecasting based on LSTM recurrent neural network publication-title: IEEE Trans. Smart Grid – volume: 212 start-page: 372 year: 2018 end-page: 385 ident: b28 article-title: Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks publication-title: Appl. Energy – volume: 8 start-page: 123369 year: 2020 ident: 10.1016/j.matcom.2023.06.017_b36 article-title: Short-term prediction of residential power energy consumption via CNN and multi-layer bi-directional LSTM networks publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2963045 – ident: 10.1016/j.matcom.2023.06.017_b38 doi: 10.1109/INDIN.2018.8471953 – volume: 9 start-page: 5271 issue: 5 year: 2018 ident: 10.1016/j.matcom.2023.06.017_b32 article-title: Deep learning for household load forecasting—A novel pooling deep RNN publication-title: IEEE Trans. Smart Grid doi: 10.1109/TSG.2017.2686012 – volume: 4 start-page: 3104 issue: January year: 2014 ident: 10.1016/j.matcom.2023.06.017_b35 article-title: Sequence to sequence learning with neural networks publication-title: Adv. Neural Inform. Process. Syst. – volume: 28 start-page: 2222 issue: 10 year: 2017 ident: 10.1016/j.matcom.2023.06.017_b7 article-title: LSTM: A search space odyssey publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2016.2582924 – year: 2019 ident: 10.1016/j.matcom.2023.06.017_b26 – ident: 10.1016/j.matcom.2023.06.017_b24 doi: 10.1109/IECON.2016.7793413 – ident: 10.1016/j.matcom.2023.06.017_b31 doi: 10.1109/ICIOT.2019.00029 – volume: 22 start-page: 679 issue: 4 year: 2006 ident: 10.1016/j.matcom.2023.06.017_b13 article-title: Another look at measures of forecast accuracy publication-title: Int. J. Forecast. doi: 10.1016/j.ijforecast.2006.03.001 – year: 2012 ident: 10.1016/j.matcom.2023.06.017_b8 – volume: 6 start-page: 91 year: 2016 ident: 10.1016/j.matcom.2023.06.017_b25 article-title: Deep learning for estimating building energy consumption publication-title: Sustain. Energy Grids Netw. doi: 10.1016/j.segan.2016.02.005 – volume: 14 start-page: 1598 issue: 6 year: 2021 ident: 10.1016/j.matcom.2023.06.017_b18 article-title: Effect of daily forecasting frequency on rolling-horizon-based EMS reducing electrical demand uncertainty in microgrids publication-title: Energies doi: 10.3390/en14061598 – volume: 9 start-page: 2120 issue: 10 year: 2019 ident: 10.1016/j.matcom.2023.06.017_b20 article-title: Towards the real-world deployment of a smart home EMS: A DP implementation on the raspberry Pi publication-title: Appl. Sci. doi: 10.3390/app9102120 – volume: 9 start-page: 4237 issue: 20 year: 2019 ident: 10.1016/j.matcom.2023.06.017_b21 article-title: Improving electric energy consumption prediction using CNN and bi-LSTM publication-title: Appl. Sci. doi: 10.3390/app9204237 – ident: 10.1016/j.matcom.2023.06.017_b34 doi: 10.1109/ICSCAN53069.2021.9526485 – volume: 142 start-page: 2791 year: 2017 ident: 10.1016/j.matcom.2023.06.017_b33 article-title: A whole system assessment of novel deep learning approach on short-term load forecasting publication-title: Energy Procedia doi: 10.1016/j.egypro.2017.12.423 – volume: 10 start-page: 841 issue: 1 year: 2019 ident: 10.1016/j.matcom.2023.06.017_b16 article-title: Short-term residential load forecasting based on LSTM recurrent neural network publication-title: IEEE Trans. Smart Grid doi: 10.1109/TSG.2017.2753802 – volume: 184 start-page: 294 year: 2021 ident: 10.1016/j.matcom.2023.06.017_b6 article-title: An artificial neural network-based forecasting model of energy-related time series for electrical grid management publication-title: Math. Comput. Simulation doi: 10.1016/j.matcom.2020.05.010 – volume: vol. 7700 start-page: 9 year: 2012 ident: 10.1016/j.matcom.2023.06.017_b22 article-title: Efficient backprop – volume: 8 start-page: 143759 year: 2020 ident: 10.1016/j.matcom.2023.06.017_b30 article-title: A novel CNN-GRU-based hybrid approach for short-term residential load forecasting publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3009537 – volume: 32 start-page: 914 issue: 3 year: 2016 ident: 10.1016/j.matcom.2023.06.017_b10 article-title: Probabilistic electric load forecasting: A tutorial review publication-title: Int. J. Forecast. doi: 10.1016/j.ijforecast.2015.11.011 – ident: 10.1016/j.matcom.2023.06.017_b29 doi: 10.1109/PTC.2019.8810899 – ident: 10.1016/j.matcom.2023.06.017_b19 doi: 10.1109/IECON.2019.8926801 – volume: 182 start-page: 72 year: 2019 ident: 10.1016/j.matcom.2023.06.017_b15 article-title: Predicting residential energy consumption using CNN-LSTM neural networks publication-title: Energy doi: 10.1016/j.energy.2019.05.230 – ident: 10.1016/j.matcom.2023.06.017_b17 doi: 10.1109/EAIT.2018.8470406 – year: 2021 ident: 10.1016/j.matcom.2023.06.017_b12 – volume: 8 start-page: 180544 year: 2020 ident: 10.1016/j.matcom.2023.06.017_b2 article-title: Hybrid CNN-LSTM model for short-term individual household load forecasting publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3028281 – ident: 10.1016/j.matcom.2023.06.017_b5 doi: 10.1109/EEEIC/ICPSEurope49358.2020.9160650 – volume: 212 start-page: 372 year: 2018 ident: 10.1016/j.matcom.2023.06.017_b28 article-title: Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks publication-title: Appl. Energy doi: 10.1016/j.apenergy.2017.12.051 – volume: 86 start-page: 1803 issue: 6 year: 2012 ident: 10.1016/j.matcom.2023.06.017_b11 article-title: A new solar radiation database for estimating PV performance in Europe and Africa publication-title: Sol. Energy doi: 10.1016/j.solener.2012.03.006 – start-page: 1 year: 2019 ident: 10.1016/j.matcom.2023.06.017_b27 – ident: 10.1016/j.matcom.2023.06.017_b3 doi: 10.1109/ISIE.2017.8001465 – start-page: 1 year: 2012 ident: 10.1016/j.matcom.2023.06.017_b9 – volume: 36 start-page: 54 issue: 1 year: 2020 ident: 10.1016/j.matcom.2023.06.017_b23 article-title: 100000 Time series and 61 forecasting methods publication-title: Int. J. Forecasting doi: 10.1016/j.ijforecast.2019.04.014 – ident: 10.1016/j.matcom.2023.06.017_b37 – year: 2015 ident: 10.1016/j.matcom.2023.06.017_b1 – volume: 133 year: 2021 ident: 10.1016/j.matcom.2023.06.017_b14 article-title: DB-Net: A novel dilated CNN based multi-step forecasting model for power consumption in integrated local energy systems publication-title: Int. J. Electr. Power Energy Syst. doi: 10.1016/j.ijepes.2021.107023 – year: 2015 ident: 10.1016/j.matcom.2023.06.017_b4 |
| SSID | ssj0007545 |
| Score | 2.430401 |
| Snippet | Energy management in smart buildings and energy communities needs short-term load demand forecasting for optimization-based scheduling, dispatch, and real-time... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 63 |
| SubjectTerms | Energy management Forecasting LSTM Residential electrical consumption |
| Title | Day-ahead forecasting of residential electric power consumption for energy management using Long Short-Term Memory encoder–decoder model |
| URI | https://dx.doi.org/10.1016/j.matcom.2023.06.017 |
| Volume | 224 |
| WOSCitedRecordID | wos001322504500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1872-7166 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0007545 issn: 0378-4754 databaseCode: AIEXJ dateStart: 19950501 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3JbtswECXcpIdeuhdN0xY89CbQsCUxpI5Bkm6ogwBxAd8ELhKqIJGN2A6SnHrOtZ_SP-qXdLhaqItuQC-CPRBt2vM8HNJv5iH0Kit2lKqpIELzAcmF1ESmVBKqlBoImUnGpBWbYIeHfDIpjnq9r6EW5uKUtS2_vCxm_9XVYANnm9LZv3B3fFEwwGNwOlzB7XD9I8fviysiIMRqwyCslJgHYjNsrBtblgteceo3jUpmRiXNcM9hirPIO6xcReBZ5MYkS3um8MEoEx1_gpSdjCGkJyPD071KTDNM04jEEycyXdnnTmanm_6OYpPYeSios5oSlpQ7b868llhkCYlkPHVFa2_60bh0llG07DfJUSOur715r989ykjzSIrz52trNTaurouZ8z_XarpfuTDNGewLhk6vJcTxNM07kdiHTbemO3GWtdXCHVyc9OFzG-qQUZK3zVxdNekPfbiPzUTMPGDTNjD_Xt9CmymjBYTSzd13B5P3MQGAeyxzNkw8VGxaWuH6e_08I-pkOeP76K7fnuBdB6sHqFe1D9G9IP2B_UrwCN1ElOEOyvC0xh2U4YAybFGGOygzo7BDGV6hDFuUYYMyvEIZdijDHmXfPn_x-MIWX4_Rx9cH4723xMt6EAXZ_oJorVUuONcDqnNBqagKLiFtrsGrClIHSHmFklJzTvOMabUzrIaMaz2kXIqMi-wJ2minbfUU4bqmVFZCCElhMdKC13VeS52qXJp9QLGFsvDVlsr3vDfSK6dlIDeelM4hpXFIaTieQ7aFSBw1cz1ffnM_C14rfd7q8tESgPbLkc_-eeQ2urP6BT1HG4vzZfUC3VYXi2Z-_tIj8jvgTcb5 |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Day-ahead+forecasting+of+residential+electric+power+consumption+for+energy+management+using+Long+Short-Term+Memory+encoder%E2%80%93decoder+model&rft.jtitle=Mathematics+and+computers+in+simulation&rft.au=La+Tona%2C+G.&rft.au=Luna%2C+M.&rft.au=Di+Piazza%2C+M.C.&rft.date=2024-10-01&rft.pub=Elsevier+B.V&rft.issn=0378-4754&rft.eissn=1872-7166&rft.volume=224&rft.spage=63&rft.epage=75&rft_id=info:doi/10.1016%2Fj.matcom.2023.06.017&rft.externalDocID=S0378475423002720 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0378-4754&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0378-4754&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0378-4754&client=summon |