Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review

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Optimal and Learning-Based Demand Response Mechanism for Electric Water Heater System

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Context aware Q-Learning-based model for decision support in the negotiation of energy contracts

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2016 International Conference on Advances in Electrical, Electronic and Systems Engineering (ICAEES)

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2013 1st IEEE Conference on Technologies for Sustainability (SusTech)

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2016 Clemson University Power Systems Conference (PSC)

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2015 IEEE 5th International Conference on Power Engineering, Energy and Electrical Drives (POWERENG)

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An Improved Fuzzy C-Means Algorithm for the Implementation of Demand Side Management Measures

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Baloğlu, U. B., & Demir, Y. (2015) · International Journal of Smart Grid and Clean Energy · doi:10.12720/sgce.4.2.132-138 · Cited by 9

Probabilistic quantification of potentially flexible residential demand

Kouzelis, K., Mendaza, I. D. D. C., & Bak‐Jensen, B. (2014) · doi:10.1109/pesgm.2014.6938836 · Cited by 8

Demand response-based voltage security improvement using artificial neural networks and sensitivity analysis

Gavgani, M. H., Abedi, M., Karimi, F., et al. (2014) · Smart Grid Conference · doi:10.1109/sgc.2014.7090863 · Cited by 8

Simulation and optimization of energy efficient operation of HVAC system as demand response with distributed energy resources

Lee, Y. M., Horesh, R., & Liberti, L. (2015) · Winter Simulation Conference · doi:10.5555/2888619.2888726 · Cited by 8

Experimental validation of the Demand Response potential of residential heating systems

Klaassen, E. A. M., Frunt, J., & Slootweg, J. G. (2016) · Power Systems Computation Conference · doi:10.1109/pscc.2016.7540825 · Cited by 8

Value assessment of aggregated energy flexibility when traded on multiple markets

MacDougall, P., Ran, B., Klever, M., et al. (2017) · International Conference on the European Energy Market (EEM) · doi:10.1109/eem.2017.7981960 · Cited by 8

Elman neural network model for short term load forecasting based on improved demand response factor

Liu, Z., Xiao, N., Wang, X., et al. (2017) · doi:10.1109/pesa.2017.8277754 · Cited by 8

Adaptive segmentation and machine learning based potential DR capacity analysis

Tang, W., Wu, Y.-S., & Yang, H. (2018) · International Conference on Harmonics and Quality of Power · doi:10.1109/ichqp.2018.8378922 · Cited by 8

Demand response driven load pattern elasticity analysis for smart households

Paterakis, N. G., Catalào, J. P. S., Taşçıkaraoğlu, A., Bakirtzis, A. G., & Erdinç, O. (2015) · International Conference on Power Engineering, Energy and Electrical Drives · doi:10.1109/powereng.2015.7266350 · Cited by 7

Load Forecasting in Demand Response

Jiang, H., & Tan, Z. (2012) · Asia-Pacific Power and Energy Engineering Conference (APPEEC) · doi:10.1109/appeec.2012.6307716 · Cited by 7

Predictive control for multi-market trade of aggregated demand response using a black box approach

MacDougall, P., Ran, B., Huitema, G., et al. (2016) · IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe) · doi:10.1109/isgteurope.2016.7856308 · Cited by 7

Modulation training method of prediction model for smart grid FastADR power limitation of building air‐conditioners

Ninagawa, C., Nakamura, A., & Morikawa, J. (2017) · IEEJ Transactions on Electrical and Electronic Engineering · doi:10.1002/tee.22533 · Cited by 7

A preliminary study towards conceptualization and implementation of a load learning model for smart automated demand response

Singh, A., Vyas, S., & Kumar, R. (2016) · IEEE Power India International Conference · doi:10.1109/poweri.2016.8077212 · Cited by 7

An Artificial Neural Network based Approach to Electric Demand Response Implementation

Kamruzzaman, M., Benidris, M., & Commuri, S. (2018) · North American Power Symposium (NAPS) · doi:10.1109/naps.2018.8600595 · Cited by 7

Multi-objective optimal scheduling method for Regional Photovoltaic-Storage-Charging Integrated system Participating in Demand Response

Jiang, T. (2019) · Renewable Power Generation Conference (RPG) · doi:10.1049/cp.2019.0383 · Cited by 7

2005 IEEE PES Transmission and Distribution Conference and Exposition

(2005) · IEEE Power and Energy Magazine · doi:10.1109/mpae.2005.1380228 · Cited by 7

2013 IEEE Computational Intelligence Applications in Smart Grid (CIASG)

(2013) · doi:10.1109/ciasg31371.2013 · Cited by 7

Neural Network Based Real-Time Pricing in Demand Side Management for Future Smart Grid

Gelažanskas, L., & Gamage, K. A. A. (2014) · IET International Conference on Power Electronics, Machines and Drives (PEMD) · doi:10.1049/cp.2014.0346 · Cited by 6

Grid reliability enhancement by peak load forecasting with a PSO hybridized ANN model

Takiyar, S. (2015) · International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) · doi:10.1109/icrito.2015.7359274 · Cited by 6

Predicting Consumer Loads for Improved Power Scheduling in Smart Homes

Behera, S., Pattnaik, B. S., Reza, M., et al. (2015) · Advances in intelligent systems and computing · doi:10.1007/978-81-322-2731-1_44 · Cited by 6

An Overview of Bio-Inspired Computing

Lakshmaiah, K., Krishna, S. A., & Reddy, B. E. (2017) · Springer eBooks · doi:10.1007/978-981-10-4741-1_42 · Cited by 6

Comparative Analysis Between Particle Swarm Optimization Algorithms Applied to Price-Based Demand Response

Cavalca, D. L., Spavieri, G., & Fernandes, R. A. S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-91253-0_31 · Cited by 6

Statistical Load Time Series Analysis for the Demand Side Management

Grabner, M., Souvent, A., Blažič, B., et al. (2018) · IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe) · doi:10.1109/isgteurope.2018.8571845 · Cited by 6

2015 Fourteenth Mexican International Conference on Artificial Intelligence (MICAI)

(2015) · doi:10.1109/micai38043.2015 · Cited by 6

Quantum-based particle swarm optimization application to studies of aggregated consumption shifting and generation scheduling in smart grids

Faria, P., Soares, J., & Vale, Z. (2014) · doi:10.1109/ciasg.2014.7011562 · Cited by 5

On load disaggregation using discrete events

Thokala, N. K., Chandra, M. G., & Nagasubramanian, K. (2016) · IEEE Innovative Smart Grid Technologies-Asia · doi:10.1109/isgt-asia.2016.7796406 · Cited by 5

Analysis of cooperative structure of demand response and market strategy of aggregator based on payoff allocation

Nishiyama, S., Sekizaki, S., Nishizaki, I., et al. (2017) · IEEE International Workshop on Computational Intelligence and Applications (IWCIA) · doi:10.1109/iwcia.2017.8203582 · Cited by 5

Unit Commitment Considering Uncertainty of Price-Based Demand Response

Aoyagi, H., Chakraborty, S., Mandal, P., et al. (2018) · IEEE PES Asia-Pacific Power and Energy Engineering Conference · doi:10.1109/appeec.2018.8566295 · Cited by 5

Quantum Particle Swarm Optimization Applied to Distinct Remuneration Approaches in Demand Response Programs

Pereira, F., Soares, J., Faria, P., et al. (2015) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci.2015.219 · Cited by 4

Demand Response in Electric Vehicles Management Optimal Use of End-User Contracts

Soares, J., Vale, Z., Morais, H. G. V., & Borges, N. (2015) · Mexican International Conference on Artificial Intelligence · doi:10.1109/micai.2015.25 · Cited by 4

Computational Models Development and Demand Response Application for Smart Grids

Pereira, R., Figueiredo, J. M. G., & Quadrado, J. C. (2016) · IFIP advances in information and communication technology · doi:10.1007/978-3-319-31165-4_32 · Cited by 4

Multi-objective PSO for scheduling electricity consumption in a smart neighborhood

Herath, P., & Venayagamoorthy, G. K. (2017) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci.2017.8285378 · Cited by 4

2015 Smart Grid Conference (SGC)

(2015) · doi:10.1109/sgc37539.2015 · Cited by 4

FLEXIBLE SMART METERING FOR MULTIPLE ENERGY VECTORS WITH ACTIVE PROSUMERS

Patti, E., Bottaccioli, L., Pons, E., et al. (2015) · PORTO Publications Open Repository TOrino (Politecnico di Torino) · Cited by 3

Energy resource aggregator managing active consumer demand programs

Spínola, J., Faria, P., & Vale, Z. (2016) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci.2016.7849856 · Cited by 3

A cooperative demand response program for smart grids

Salami, A., & Farsi, M. M. (2015) · Smart Grid Conference · doi:10.1109/sgc.2015.7857417 · Cited by 3

Bilateral contract prices estimation using a Q-leaming based approach

Rodriguez-Fernandez, J., Pinto, T. D. O., Silva, F., et al. (2017) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci.2017.8285198 · Cited by 3

Interruptible Load Management Strategy Based on Chamberlain Model

Xie, Z., Li, X., Xu, T., et al. (2018) · Communications in computer and information science · doi:10.1007/978-981-13-2203-7_41 · Cited by 3

Energy Resource Scheduling with Multiple Iterations for the Validation of Demand Response Aggregation

Spínola, J., Faria, P., & Vale, Z. (2018) · IEEE PES Transmission and Distribution Conference and Exhibition · doi:10.1109/tdc.2018.8440143 · Cited by 2

Aggregation validation approach for the management of resources in a smart grid

Spínola, J., Faria, P., & Vale, Z. (2016) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci.2016.7849861 · Cited by 1

Demand Response clustering

Waczowicz, S., Ludwig, N., Ordiano, J. Á. G., et al. (2018) · International Conference on Future Energy Systems · doi:10.1145/3208903.3212049 · Cited by 1

2016 IEEE 9th International Workshop on Computational Intelligence and Applications (IWCIA)

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Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm

Ahmad, T., Madonski, R., Zhang, D., Huang, C., & Mujeeb, A. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2022.112128 · Cited by 711

Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review

Bibri, S. E., Krogstie, J., Kaboli, A., & Alahi, A. (2024) · Environmental Science and Ecotechnology · doi:10.1016/j.ese.2023.100330 · Cited by 536

Prospects and Challenges of the Machine Learning and Data-Driven Methods for the Predictive Analysis of Power Systems: A Review

Strielkowski, W., Vlasov, A., Selivanov, K., Muraviev, K., & Shakhnov, V. (2023) · Energies · doi:10.3390/en16104025 · Cited by 237

Tackling climate change with machine learning

Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336

Artificial Intelligence Enabled Demand Response: Prospects and Challenges in Smart Grid Environment

Khan, M. A., Saleh, A. M., Waseem, M., & Sajjad, I. A. (2023) · IEEE Access · doi:10.1109/access.2022.3231444 · Cited by 179

Changes in flexibility of elite female soccer players resulting from a flexibility program or combined flexibility and strength program: A pilot study

Schmitt, G., Pelham, T. W., & Holt, L. E. (1999) · Excerpta medica. Section 35, Occupational health and industrial medicine · Cited by 3

Workplace flexibility: Does policy influence physical activity and sedentary behaviour in employees?

Olsen, H. M., Brown, W. J., & Burton, N. W. (2014) · Journal of science and medicine in sport · doi:10.1016/j.jsams.2014.11.026

STRETCHING AND FLEXIBILITY: A RANGE OF MOTION FOR GAMES AND SPORTS

Rahman, M. H., & Islam, M. S. (2020) · European Journal of Literature, Language and Linguistics Studies - Open Journal Systems · doi:10.46827/ejpe.v6i8.3380 · Cited by 26

Prerequisites for stretching use in physical education of high school female students

Bіelіkova, N., & Деделюк, Н. А. (2020) · Pedagogy and Psychology of Sport · doi:10.12775/pps.2020.06.04.016

Flexibility of Production Control Systems

Takahashi, K., Hiraki, S., & Soshiroda, M. (1991) · doi:10.1007/978-3-642-76401-1_4 · Cited by 1