{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T15:12:37Z","timestamp":1781622757086,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":53,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,6,28]],"date-time":"2022-06-28T00:00:00Z","timestamp":1656374400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,6,28]]},"DOI":"10.1145\/3538637.3538871","type":"proceedings-article","created":{"date-parts":[[2022,6,22]],"date-time":"2022-06-22T16:33:05Z","timestamp":1655915585000},"page":"418-429","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Demand response model identification and behavior forecast with OptNet"],"prefix":"10.1145","author":[{"given":"Yuexin","family":"Bian","sequence":"first","affiliation":[{"name":"University of California San Diego"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ningkun","family":"Zheng","sequence":"additional","affiliation":[{"name":"Columbia University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Zheng","sequence":"additional","affiliation":[{"name":"University of California San Diego"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bolun","family":"Xu","sequence":"additional","affiliation":[{"name":"Columbia University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanyuan","family":"Shi","sequence":"additional","affiliation":[{"name":"University of California San Diego"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,6,28]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2014.07.098"},{"key":"e_1_3_2_1_2_1","volume-title":"Stein et al., \"Understanding variability and uncertainty of photovoltaics for integration with the electric power system,\" Lawrence Berkeley National Lab.(LBNL)","author":"Mills A.","year":"2009","unstructured":"A. Mills , M. Ahlstrom , M. Brower , A. Ellis , R. George , T. Hoff , B. Kroposki , C. Lenox , N. Miller , J. Stein et al., \"Understanding variability and uncertainty of photovoltaics for integration with the electric power system,\" Lawrence Berkeley National Lab.(LBNL) , Berkeley, CA (United States), Tech. Rep ., 2009 . A. Mills, M. Ahlstrom, M. Brower, A. Ellis, R. George, T. Hoff, B. Kroposki, C. Lenox, N. Miller, J. Stein et al., \"Understanding variability and uncertainty of photovoltaics for integration with the electric power system,\" Lawrence Berkeley National Lab.(LBNL), Berkeley, CA (United States), Tech. Rep., 2009."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.2974848"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2019.03.038"},{"key":"e_1_3_2_1_5_1","first-page":"1368","volume-title":"IEEE","author":"Chen Y.","year":"2017","unstructured":"Y. Chen , Y. Shi , and B. Zhang , \" Modeling and optimization of complex building energy systems with deep neural networks,\" in 2017 51st Asilomar Conference on Signals, Systems, and Computers . IEEE , 2017 , pp. 1368 -- 1373 . Y. Chen, Y. Shi, and B. Zhang, \"Modeling and optimization of complex building energy systems with deep neural networks,\" in 2017 51st Asilomar Conference on Signals, Systems, and Computers. IEEE, 2017, pp. 1368--1373."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2017.2749512"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2015.01.025"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2014.07.064"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2012.2195037"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enpol.2017.07.007"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/SURV.2011.122111.00145"},{"key":"e_1_3_2_1_13_1","first-page":"136","volume-title":"PMLR","author":"Amos B.","year":"2017","unstructured":"B. Amos and J. Z. Kolter , \" Optnet: Differentiable optimization as a layer in neural networks,\" in International Conference on Machine Learning . PMLR , 2017 , pp. 136 -- 145 . B. Amos and J. Z. Kolter, \"Optnet: Differentiable optimization as a layer in neural networks,\" in International Conference on Machine Learning. PMLR, 2017, pp. 136--145."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2021.116791"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10100-020-00699-1"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enpol.2021.112290"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1049\/iet-gtd.2015.0401"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2017.2739021"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2014.2307474"},{"key":"e_1_3_2_1_20_1","volume-title":"Energy","author":"Qdr Q.","year":"2006","unstructured":"Q. Qdr , \"Benefits of demand response in electricity markets and recommendations for achieving them,\" US Dept . Energy , Washington, DC, USA , Tech. Rep , vol. 2006 , 2006 . Q. Qdr, \"Benefits of demand response in electricity markets and recommendations for achieving them,\" US Dept. Energy, Washington, DC, USA, Tech. Rep, vol. 2006, 2006."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2020.2983388"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2015.2409053"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2018.2795007"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2016.2530843"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2020.2997956"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2014.2341556"},{"key":"e_1_3_2_1_27_1","first-page":"224","volume-title":"IEEE","author":"Xu B.","year":"2020","unstructured":"B. Xu , M. Korp\u00e5s , A. Botterud , and F. O'Sullivan , \"A lagrangian policy for optimal energy storage control,\" in 2020 American Control Conference (ACC) . IEEE , 2020 , pp. 224 -- 230 . B. Xu, M. Korp\u00e5s, A. Botterud, and F. O'Sullivan, \"A lagrangian policy for optimal energy storage control,\" in 2020 American Control Conference (ACC). IEEE, 2020, pp. 224--230."},{"key":"e_1_3_2_1_28_1","first-page":"2919","volume-title":"IEEE","author":"Nghiem T. X.","year":"2017","unstructured":"T. X. Nghiem and C. N. Jones , \" Data-driven demand response modeling and control of buildings with gaussian processes,\" in 2017 American Control Conference (ACC) . IEEE , 2017 , pp. 2919 -- 2924 . T. X. Nghiem and C. N. Jones, \"Data-driven demand response modeling and control of buildings with gaussian processes,\" in 2017 American Control Conference (ACC). IEEE, 2017, pp. 2919--2924."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2020.118019"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2017.2671743"},{"key":"e_1_3_2_1_31_1","volume-title":"Learning protein structure with a differentiable simulator,\" in International Conference on Learning Representations","author":"Ingraham J.","year":"2018","unstructured":"J. Ingraham , A. Riesselman , C. Sander , and D. Marks , \" Learning protein structure with a differentiable simulator,\" in International Conference on Learning Representations , 2018 . J. Ingraham, A. Riesselman, C. Sander, and D. Marks, \"Learning protein structure with a differentiable simulator,\" in International Conference on Learning Representations, 2018."},{"key":"e_1_3_2_1_32_1","first-page":"6545","volume-title":"PMLR","author":"Wang P.-W.","year":"2019","unstructured":"P.-W. Wang , P. Donti , B. Wilder , and Z. Kolter , \" Satnet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver,\" in International Conference on Machine Learning . PMLR , 2019 , pp. 6545 -- 6554 . P.-W. Wang, P. Donti, B. Wilder, and Z. Kolter, \"Satnet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver,\" in International Conference on Machine Learning. PMLR, 2019, pp. 6545--6554."},{"key":"e_1_3_2_1_33_1","volume-title":"Ba-net: Dense bundle adjustment network,\" arXiv preprint arXiv:1806.04807","author":"Tang C.","year":"2018","unstructured":"C. Tang and P. Tan , \" Ba-net: Dense bundle adjustment network,\" arXiv preprint arXiv:1806.04807 , 2018 . C. Tang and P. Tan, \"Ba-net: Dense bundle adjustment network,\" arXiv preprint arXiv:1806.04807, 2018."},{"key":"e_1_3_2_1_34_1","volume-title":"Differentiable mpc for end-to-end planning and control,\" arXiv preprint arXiv:1810.13400","author":"Amos B.","year":"2018","unstructured":"B. Amos , I. D. J. Rodriguez , J. Sacks , B. Boots , and J. Z. Kolter , \" Differentiable mpc for end-to-end planning and control,\" arXiv preprint arXiv:1810.13400 , 2018 . B. Amos, I. D. J. Rodriguez, J. Sacks, B. Boots, and J. Z. Kolter, \"Differentiable mpc for end-to-end planning and control,\" arXiv preprint arXiv:1810.13400, 2018."},{"key":"e_1_3_2_1_35_1","first-page":"657","article-title":"Meta-learning with differentiable convex optimization","author":"Lee K.","year":"2019","unstructured":"K. Lee , S. Maji , A. Ravichandran , and S. Soatto , \" Meta-learning with differentiable convex optimization ,\" in Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition , 2019 , pp. 10 657 -- 610 665. K. Lee, S. Maji, A. Ravichandran, and S. Soatto, \"Meta-learning with differentiable convex optimization,\" in Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 10 657--10 665.","journal-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"key":"e_1_3_2_1_36_1","first-page":"7178","volume-title":"End-to-end differentiable physics for learning and control,\" Advances in neural information processing systems","author":"de Avila Belbute-Peres F.","year":"2018","unstructured":"F. de Avila Belbute-Peres , K. Smith , K. Allen , J. Tenenbaum , and J. Z. Kolter , \" End-to-end differentiable physics for learning and control,\" Advances in neural information processing systems , vol. 31 , pp. 7178 -- 7189 , 2018 . F. de Avila Belbute-Peres, K. Smith, K. Allen, J. Tenenbaum, and J. Z. Kolter, \"End-to-end differentiable physics for learning and control,\" Advances in neural information processing systems, vol. 31, pp. 7178--7189, 2018."},{"key":"e_1_3_2_1_37_1","volume-title":"Task-based end-to-end model learning in stochastic optimization,\" arXiv preprint arXiv:1703.04529","author":"Donti P. L.","year":"2017","unstructured":"P. L. Donti , B. Amos , and J. Z. Kolter , \" Task-based end-to-end model learning in stochastic optimization,\" arXiv preprint arXiv:1703.04529 , 2017 . P. L. Donti, B. Amos, and J. Z. Kolter, \"Task-based end-to-end model learning in stochastic optimization,\" arXiv preprint arXiv:1703.04529, 2017."},{"key":"e_1_3_2_1_38_1","first-page":"540","volume-title":"IEEE","author":"Jacquot P.","year":"2017","unstructured":"P. Jacquot , O. Beaude , S. Gaubert , and N. Oudjane , \" Demand response in the smart grid: The impact of consumers temporal preferences,\" in 2017 IEEE International Conference on Smart Grid Communications (SmartGridComm) . IEEE , 2017 , pp. 540 -- 545 . P. Jacquot, O. Beaude, S. Gaubert, and N. Oudjane, \"Demand response in the smart grid: The impact of consumers temporal preferences,\" in 2017 IEEE International Conference on Smart Grid Communications (SmartGridComm). IEEE, 2017, pp. 540--545."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2015.2431324"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2015.12.039"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511804441"},{"key":"e_1_3_2_1_42_1","unstructured":"S. Barratt \"On the differentiability of the solution to convex optimization problems \" arXiv preprint arXiv:1804.05098 2018.  S. Barratt \"On the differentiability of the solution to convex optimization problems \" arXiv preprint arXiv:1804.05098 2018."},{"key":"e_1_3_2_1_43_1","first-page":"1","volume-title":"IEEE","author":"Baker K.","year":"2013","unstructured":"K. Baker , D. Zhu , G. Hug , and X. Li , \" Jacobian singularities in optimal power flow problems caused by intertemporal constraints,\" in 2013 North American Power Symposium (NAPS) . IEEE , 2013 , pp. 1 -- 6 . K. Baker, D. Zhu, G. Hug, and X. Li, \"Jacobian singularities in optimal power flow problems caused by intertemporal constraints,\" in 2013 North American Power Symposium (NAPS). IEEE, 2013, pp. 1--6."},{"key":"e_1_3_2_1_44_1","first-page":"287","volume-title":"PMLR","author":"Furieri L.","year":"2020","unstructured":"L. Furieri , Y. Zheng , and M. Kamgarpour , \" Learning the globally optimal distributed lq regulator,\" in Learning for Dynamics and Control . PMLR , 2020 , pp. 287 -- 297 . L. Furieri, Y. Zheng, and M. Kamgarpour, \"Learning the globally optimal distributed lq regulator,\" in Learning for Dynamics and Control. PMLR, 2020, pp. 287--297."},{"issue":"4","key":"e_1_3_2_1_45_1","first-page":"643","article-title":"Gradient methods for minimizing functionals","volume":"3","author":"Polyak B. T.","year":"1963","unstructured":"B. T. Polyak , \" Gradient methods for minimizing functionals ,\" Zhurnal Vychislitel'noi Matematiki i Matematicheskoi Fiziki , vol. 3 , no. 4 , pp. 643 -- 653 , 1963 . B. T. Polyak, \"Gradient methods for minimizing functionals,\" Zhurnal Vychislitel'noi Matematiki i Matematicheskoi Fiziki, vol. 3, no. 4, pp. 643--653, 1963.","journal-title":"Zhurnal Vychislitel'noi Matematiki i Matematicheskoi Fiziki"},{"key":"e_1_3_2_1_46_1","first-page":"795","volume-title":"Linear convergence of gradient and proximal-gradient methods under the polyak-\u0142ojasiewicz condition,\" in Joint European Conference on Machine Learning and Knowledge Discovery in Databases","author":"Karimi H.","year":"2016","unstructured":"H. Karimi , J. Nutini , and M. Schmidt , \" Linear convergence of gradient and proximal-gradient methods under the polyak-\u0142ojasiewicz condition,\" in Joint European Conference on Machine Learning and Knowledge Discovery in Databases . Springer , 2016 , pp. 795 -- 811 . H. Karimi, J. Nutini, and M. Schmidt, \"Linear convergence of gradient and proximal-gradient methods under the polyak-\u0142ojasiewicz condition,\" in Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 2016, pp. 795--811."},{"key":"e_1_3_2_1_47_1","first-page":"1","volume-title":"PMLR","author":"Pedregosa F.","year":"2020","unstructured":"F. Pedregosa , G. Negiar , A. Askari , and M. Jaggi , \" Linearly convergent frankwolfe with backtracking line-search,\" in International Conference on Artificial Intelligence and Statistics . PMLR , 2020 , pp. 1 -- 10 . F. Pedregosa, G. Negiar, A. Askari, and M. Jaggi, \"Linearly convergent frankwolfe with backtracking line-search,\" in International Conference on Artificial Intelligence and Statistics. PMLR, 2020, pp. 1--10."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.107014"},{"key":"e_1_3_2_1_49_1","first-page":"1339","volume-title":"PMLR","author":"Du S.","year":"2018","unstructured":"S. Du , J. Lee , Y. Tian , A. Singh , and B. Poczos , \" Gradient descent learns one-hidden-layer cnn: Don't be afraid of spurious local minima,\" in International Conference on Machine Learning . PMLR , 2018 , pp. 1339 -- 1348 . S. Du, J. Lee, Y. Tian, A. Singh, and B. Poczos, \"Gradient descent learns one-hidden-layer cnn: Don't be afraid of spurious local minima,\" in International Conference on Machine Learning. PMLR, 2018, pp. 1339--1348."},{"key":"e_1_3_2_1_50_1","first-page":"2698","volume-title":"PMLR","author":"Kleinberg B.","year":"2018","unstructured":"B. Kleinberg , Y. Li , and Y. Yuan , \" An alternative view: When does sgd escape local minima?\" in International Conference on Machine Learning . PMLR , 2018 , pp. 2698 -- 2707 . B. Kleinberg, Y. Li, and Y. Yuan, \"An alternative view: When does sgd escape local minima?\" in International Conference on Machine Learning. PMLR, 2018, pp. 2698--2707."},{"key":"e_1_3_2_1_51_1","first-page":"4255","volume-title":"IEEE","author":"Feng H.","year":"2020","unstructured":"H. Feng , H. Zhang , and J. Lavaei ,\" A dynamical system perspective for escaping sharp local minima in equality constrained optimization problems,\" in 2020 59th IEEE Conference on Decision and Control (CDC) . IEEE , 2020 , pp. 4255 -- 4261 . H. Feng, H. Zhang, and J. Lavaei,\" A dynamical system perspective for escaping sharp local minima in equality constrained optimization problems,\" in 2020 59th IEEE Conference on Decision and Control (CDC). IEEE, 2020, pp. 4255--4261."},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-019-09719-2"},{"key":"e_1_3_2_1_53_1","unstructured":"\"New york independent system operator - energy market and operation data.\" [Online]. Available: https:\/\/www.nyiso.com\/energy-market-operational-data  \"New york independent system operator - energy market and operation data.\" [Online]. Available: https:\/\/www.nyiso.com\/energy-market-operational-data"},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2016.04.064"}],"event":{"name":"e-Energy '22: The Thirteenth ACM International Conference on Future Energy Systems","location":"Virtual Event","acronym":"e-Energy '22","sponsor":["SIGEnergy ACM Special Interest Group on Energy Systems and Informatics"]},"container-title":["Proceedings of the Thirteenth ACM International Conference on Future Energy Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3538637.3538871","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3538637.3538871","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:03:02Z","timestamp":1750186982000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3538637.3538871"}},"subtitle":["a gradient-based approach"],"short-title":[],"issued":{"date-parts":[[2022,6,28]]},"references-count":53,"alternative-id":["10.1145\/3538637.3538871","10.1145\/3538637"],"URL":"https:\/\/doi.org\/10.1145\/3538637.3538871","relation":{},"subject":[],"published":{"date-parts":[[2022,6,28]]},"assertion":[{"value":"2022-06-28","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}