{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T15:19:56Z","timestamp":1769008796798,"version":"3.49.0"},"reference-count":35,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2019,2,10]],"date-time":"2019-02-10T00:00:00Z","timestamp":1549756800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005416","name":"Norges Forskningsr\u00e5d","doi-asserted-by":"publisher","award":["267967"],"award-info":[{"award-number":["267967"]}],"id":[{"id":"10.13039\/501100005416","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010661","name":"Horizon 2020 Framework Programme","doi-asserted-by":"publisher","award":["646578"],"award-info":[{"award-number":["646578"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The progress of technology on energy and IoT fields has led to an increasingly complicated electric environment in low-voltage local microgrid, along with the extensions of electric vehicle, micro-generation, and local storage. It is required to establish a home energy management system (HEMS) to efficiently integrate and manage household energy micro-generation, consumption and storage, in order to realize decentralized local energy systems at the community level. Domestic power demand prediction is of great importance for establishing HEMS on realizing load balancing as well as other smart energy solutions with the support of IoT techniques. Artificial neural networks with various network types (e.g., DNN, LSTM\/GRU based RNN) and other configurations are widely utilized on energy predictions. However, the selection of network configuration for each research is generally a case by case study achieved through empirical or enumerative approaches. Moreover, the commonly utilized network initialization methods assign parameter values based on random numbers, which cause diversity on model performance, including learning efficiency, forecast accuracy, etc. In this paper, an evolutionary ensemble neural network pool (EENNP) method is proposed to achieve a population of well-performing networks with proper combinations of configuration and initialization automatically. In the experimental study, power demand predictions of multiple households are explored in three application scenarios: optimizing potential network configuration set, forecasting single household power demand, and refilling missing data. The impacts of evolutionary parameters on model performance are investigated. The experimental results illustrate that the proposed method achieves better solutions on the considered scenarios. The optimized potential network configuration set using EENNP achieves a similar result to manual optimization. The results of household demand prediction and missing data refilling perform better than the na\u00efve and simple predictors.<\/jats:p>","DOI":"10.3390\/s19030721","type":"journal-article","created":{"date-parts":[[2019,2,12]],"date-time":"2019-02-12T03:18:20Z","timestamp":1549941500000},"page":"721","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["Household Power Demand Prediction Using Evolutionary Ensemble Neural Network Pool with Multiple Network Structures"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7111-0771","authenticated-orcid":false,"given":"Songpu","family":"Ai","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, University of Stavanger, 4036 Stavanger, Norway"}]},{"given":"Antorweep","family":"Chakravorty","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, University of Stavanger, 4036 Stavanger, Norway"}]},{"given":"Chunming","family":"Rong","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, University of Stavanger, 4036 Stavanger, Norway"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ravichandran, A., Malysz, P., Sirouspour, S., and Emadi, A. (2013, January 16\u201319). The critical role of microgrids in transition to a smarter grid: A technical review. Proceedings of the 2013 IEEE Transportation Electrification Conference and Expo (ITEC), Metro Detroit, MI, USA.","DOI":"10.1109\/ITEC.2013.6573507"},{"key":"ref_2","unstructured":"Liu, J., Li, X., Chen, X., Zhen, Y., and Zeng, L. (2011, January 13\u201316). Applications of Internet of Things on smart grid in China. Proceedings of the 13th International Conference on Advanced Communication Technology (ICACT 2011), Seoul, Korea."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Choi, H., Lee, C., Shim, M., Han, J., and Baek, Y. (2018). Design of an Artificial Neural Network Algorithm for a Low-Cost Insole Sensor to Estimate the Ground Reaction Force (GRF) and Calibrate the Center of Pressure (CoP). Sensors, 18.","DOI":"10.3390\/s18124349"},{"key":"ref_4","unstructured":"Azzam ul, A., Hassnain, S.R.U., and Khan, A. (2007, January 12\u201317). Short Term Load Forecasting Using Particle Swarm Optimization Based ANN Approach. Proceedings of the 2007 International Joint Conference on Neural Networks, Orlando, FL, USA."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Bier, T., Abdeslam, D.O., Merckle, J., and Benyoucef, D. (2012, January 25\u201328). Smart meter systems detection & classification using artificial neural networks. Proceedings of the IECON 2012-38th Annual Conference on IEEE Industrial Electronics Society, Montreal, QC, Canada.","DOI":"10.1109\/IECON.2012.6389365"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.enbuild.2012.12.009","article-title":"Upgrade of an artificial neural network prediction method for electrical consumption forecasting using an hourly temperature curve model","volume":"60","year":"2013","journal-title":"Energy Build."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Songpu, A., Kolhe, M.L., Jiao, L., and Zhang, Q. (2015, January 7\u20139). Domestic load forecasting using neural network and its use for missing data analysis. Proceedings of the 2015 9th International Symposium on Advanced Topics in Electrical Engineering (ATEE), Glasgow, Scotland.","DOI":"10.1109\/ATEE.2015.7133866"},{"key":"ref_8","unstructured":"Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. (arXiv, 2014). Empirical evaluation of gated recurrent neural networks on sequence modeling, arXiv."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Hochreiter, S., and Schmidhuber, J. (1997). Long Short-Term Memory, MIT Press.","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Cho, K., Van Merri\u00ebnboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (arXiv, 2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation, arXiv.","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref_11","unstructured":"Hermans, M., and Schrauwen, B. (2013). Training and analysing deep recurrent neural networks. Advances in Neural Information Processing Systems, MIT Press."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Gao, M., Shi, G., and Li, S. (2018). Online Prediction of Ship Behavior with Automatic Identification System Sensor Data Using Bidirectional Long Short-Term Memory Recurrent Neural Network. Sensors, 18.","DOI":"10.3390\/s18124211"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Marino, D.L., Amarasinghe, K., and Manic, M. (2016, January 23\u201326). Building energy load forecasting using Deep Neural Networks. Proceedings of the IECON 2016\u201442nd Annual Conference of the IEEE Industrial Electronics Society, Florence, Italy.","DOI":"10.1109\/IECON.2016.7793413"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Heghedus, C., Chakravorty, A., and Rong, C. (2018, January 21\u201325). Energy Load Forecasting Using Deep Learning. Proceedings of the 2018 IEEE International Conference on Energy Internet (ICEI), Beijing, China.","DOI":"10.1109\/ICEI.2018.00-23"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kumar, S., Hussain, L., Banarjee, S., and Reza, M. (2018, January 12\u201313). Energy Load Forecasting using Deep Learning Approach-LSTM and GRU in Spark Cluster. Proceedings of the 2018 Fifth International Conference on Emerging Applications of Information Technology (EAIT); IIEST, Howrah, India.","DOI":"10.1109\/EAIT.2018.8470406"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Kabir, H.M.D., Khosravi, A., Hosen, M.A., and Nahavandi, S. (2018). Neural Network-based Uncertainty Quantification: A Survey of Methodologies and Applications. IEEE Access.","DOI":"10.1109\/ACCESS.2018.2836917"},{"key":"ref_17","unstructured":"Ai, S. (2015). Domestic Electricity Demand and Peak Predictions Considering Influence of Weather Parameters. [Master\u2019s Thesis, University of Agder]."},{"key":"ref_18","unstructured":"Nielsen, M.A. (2015). Neural Networks and Deep Learning, Determination Press."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Fang, H., and He, L. (2012, January 11\u201313). BP neural network for human activity recognition in smart home. Proceedings of the 2012 International Conference on Computer Science and Service System (CSSS), Nanjing, China.","DOI":"10.1109\/CSSS.2012.262"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1109\/MCI.2011.941590","article-title":"Short-Term Load Forecasting with Neural Network Ensembles: A Comparative Study [Application Notes]","volume":"6","author":"Felice","year":"2011","journal-title":"IEEE Comput. Intell. Mag."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.enbuild.2015.11.017","article-title":"ANN\u2013GA smart appliance scheduling for optimised energy management in the domestic sector","volume":"111","author":"Yuce","year":"2016","journal-title":"Energy Build."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1016\/S0378-4371(00)00479-9","article-title":"Using genetic algorithms to select architecture of a feedforward artificial neural network","volume":"289","author":"Arifovic","year":"2001","journal-title":"Phys. A Stat. Mech. Appl."},{"key":"ref_23","first-page":"6","article-title":"Neural Networks using Genetic Algorithms","volume":"77","author":"Mahajan","year":"2014","journal-title":"Int. J. Comput. Appl. Technol."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Sak, H., Senior, A., and Beaufays, F. (arXiv, 2014). Long short-term memory based recurrent neural network architectures for large vocabulary speech recognition, arXiv.","DOI":"10.21437\/Interspeech.2014-80"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/72.279181","article-title":"Learning long-term dependencies with gradient descent is difficult","volume":"5","author":"Bengio","year":"1994","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_26","unstructured":"Glorot, X., and Bengio, Y. (2010, January 13\u201315). Understanding the difficulty of training deep feedforward neural networks. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, Sardinia, Italy."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ai, S., Chakravorty, A., and Rong, C. (2019, January 11\u201313). LSTM based Household Peak Demand Prediction. Proceedings of the 2019 International Conference on AI in Information and Communication, Okinawa, Japan.","DOI":"10.1109\/ICAIIC.2019.8668971"},{"key":"ref_28","unstructured":"Fogel, D.B. (1997, January 1\u20132). The Advantages of Evolutionary Computation. Proceedings of the BCEC 1997, Skovde, Sweden."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1007\/s00365-006-0663-2","article-title":"On early stopping in gradient descent learning","volume":"26","author":"Yao","year":"2007","journal-title":"Constr. Approx."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhou, Z.-H. (2012). Ensemble Methods: Foundations and Algorithms, CRC Press.","DOI":"10.1201\/b12207"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wichard, J.D. (2016, January 24\u201329). An adaptive forecasting strategy with hybrid ensemble models. Proceedings of the 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, Canada.","DOI":"10.1109\/IJCNN.2016.7727375"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ai, S., Chakravorty, A., and Rong, C. (2018, January 21\u201325). Household EV Charging Demand Prediction using Machine and Ensemble Learning. Proceedings of the 2018 IEEE International Conference on Energy Internet (ICEI), Beijing, China.","DOI":"10.1109\/ICEI.2018.00037"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.enbuild.2014.08.004","article-title":"Neural network model ensembles for building-level electricity load forecasts","volume":"84","author":"Jetcheva","year":"2014","journal-title":"Energy Build."},{"key":"ref_34","unstructured":"(2018, December 15). Python. Available online: https:\/\/www.python.org\/."},{"key":"ref_35","unstructured":"(2018, December 15). TensorFlow. Available online: https:\/\/www.tensorflow.org\/."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/3\/721\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:31:00Z","timestamp":1760185860000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/3\/721"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,2,10]]},"references-count":35,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2019,2]]}},"alternative-id":["s19030721"],"URL":"https:\/\/doi.org\/10.3390\/s19030721","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,2,10]]}}}