References
- A. Blázquez-García, A. Conde, U. Mori and J. A. Lozano, “A review on outlier/anomaly detection in time series data,” ACM Comput. Surveys (CSUR), vol. 54, no. 3, pp. 1–33, 2021.
- Z. Wadud, S. Royston and J. Selby, “Modeling energy demand from higher education institutions: A case study of the UK?,” Appl. Energy., vol. 233–234, pp. 816–826, 2018. DOI: 10.1016/j.apenergy.2018.09.203.
- Y. Himeur, A. Alsalemi, F. Bensaali and A. Amira, “Building power consumption datasets: survey, taxonomy and future directions,” Energy Build., vol. 227, pp. 110404, 2020. DOI: 10.1016/j.enbuild.2020.110404.
- M. Fayaz and D. Kim, “A prediction methodology of energy consumption based on deep extreme learning machine and comparative analysis in residential buildings,” Electron., vol. 7, no. 10, pp. 222, 2018. DOI: 10.3390/electronics7100222.
- S. Bourhnane, M. R. Abid, R. Loghoul, K. Zine-Dine, N. Elkamoun and D. Benhaddou, Machine Learning for Energy Consumption Prediction and Scheduling in Smart Buildings, Springer Nature: Switzerland AG, 2020. DOI: 10.1007/s42452-020-2024-9.
- M. Khoshbakht, Z. Gou and K. Dupre, “Energy use characteristics and benchmarking for higher education buildings,” Energy Build., vol. 164, pp. 61–76, 2018. DOI: 10.1016/j.enbuild.2018.01.001.
- H. Cai, S. Shen, Q. Lin, X. Li and H. Xiao, “Predicting the energy consumption of residential buildings for regional electricity supply-side and demand-side management,” IEEE Access, vol. 7, pp. 30386–30397, 2019. DOI: 10.1109/ACCESS.2019.2901257.
- C. Mokhtara, B. Negrou, A. Bouferrouk, Y. Yao, N. Settou and M. Ramadan, “Integrated supply-demand energy management for the optimal design of off-grid hybrid renewable energy systems for residential electrification in arid climates,” Energy Convers. Manage., vol. 221, no. July, pp. 113192, 2020. DOI: 10.1016/j.enconman.2020.113192.
- P. Goodwin, F. Petropoulos and R. J. Hyndman, “A note on upper bounds for forecast-value-added relative to naïve forecasts,” J. Oper. Res. Soc., vol. 68, no. 9, pp. 1082–1084, 2017. DOI: 10.1057/s41274-017-0218-3.
- J. Fattah, L. Ezzine, Z. Aman, H. El Moussami and A. Lachhab, “Forecasting of demand using ARIMA model,” Int. J. Eng. Bus. Manage., vol. 10, pp. 184797901880867–9, 2018. DOI: 10.1177/1847979018808673.
- Ü. Ç. Büyükşahin and Ş. Ertekin, “Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition,” Neurocomputing, vol. 361, pp. 151–163, 2019. DOI: 10.1016/j.neucom.2019.05.099.
- R. Tugay and ŞG. Ögüdücü, 2017. “Demand prediction using machine learning methods and stacked generalization,” DATA 2017 – Proceedings of the 6th International Conference on Data Science, Technology and Applications, pp. 216–222. DOI: 10.5220/0006431602160222.
- L. Yu, S. Qin, M. Zhang, C. Shen, T. Jiang and X. Guan, “Deep reinforcement learning for smart building energy management: a survey,” ArXiv, no. 1, pp. 1–21, 2020. ArXiv preprint arXiv:2008.05074.
- K. Nikolopoulos, “We need to talk about intermittent demand forecasting,” Eur. J. Oper. Res., vol. 291, no. 2, pp. 549–559, 2021. DOI: 10.1016/j.ejor.2019.12.046.
- N. Somu, G. Raman M R and K. Ramamritham, “A deep learning framework for building energy consumption forecast,” Renew. Sustain. Energy Rev., vol. 137, pp. 110591, 2021. DOI: 10.1016/j.rser.2020.110591.
- J. Bedi and D. Toshniwal, “Deep learning framework to forecast electricity demand,” Appl. Energy, vol. 238, pp. 1312–1326, 2019. DOI: 10.1016/j.apenergy.2019.01.113.
- Y. Qin, D. Song, H. Cheng, W. Cheng, G. Jiang and G. W. Cottrell, “A dual-stage attention-based recurrent neural network for time series prediction,” IJCAI Int. Joint Conf. Artif. Intell., vol. 0, pp. 2627–2633, 2017. DOI: 10.24963/ijcai.2017/366.
- A. Acakpovi, A. T. Ternor, N. Y. Asabere, P. Adjei and A. S. Iddrisu, “Time Series Prediction of Electricity Demand Using Adaptive Neuro-Fuzzy Inference Systems,” Math. Prob. Eng., vol. 2020, pp. 1–14, 2020. DOI: 10.1155/2020/4181045.
- S. S. Namin and A. S. Namin, “Forecasting economic and financial time series: Arima vs,” LSTM. ArXiv, no. 1, pp. 1–19, 2018.
- S. Siami-Namini, N. Tavakoli and A. Siami Namin, 2019. “A comparison of ARIMA and LSTM in forecasting time series,” Proceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018, pp. 1394–1401. DOI: 10.1109/ICMLA.2018.00227.
- B. O. Agajelu, O. G. Ekwueme, N. S. P. Obuka and G. O. R. Ikwu, “Life cycle cost analysis of a diesel/photovoltaic hybrid power generating system,” Int. Inst. Sci. Technol. Educ. (IISTE), vol. 3, no. 1, pp. 19–31, 2013.
- C. Ghenai and M. Bettayeb, “Modeling and performance analysis of a stand-alone hybrid solar PV/fuel cell/diesel generator power system for a university building,” Energy, vol. 171, pp. 180–189, 2019. DOI: 10.1016/j.energy.2019.01.019.
- F. Wang, Z. Xuan, Z. Zhen, K. Li, T. Wang and M. Shi, “A day-ahead PV power forecasting method based on the LSTM-RNN model and time correlation modification under a partial daily pattern prediction framework,” Energy Convers. Manage., vol. 212, no. February, pp. 112766, 2020. DOI: 10.1016/j.enconman.2020.112766.
- R. Li, P. Jiang, H. Yang and C. Li, “A novel hybrid forecasting scheme for electricity demand time series,” Sustain. Cities Soc., vol. 55, pp. 102036, 2020. DOI: 10.1016/j.scs.2020.102036.
- B. K. Das and F. Zaman, “Performance analysis of a PV/Diesel hybrid system for a remote area in Bangladesh: effects of dispatch strategies, batteries, and generator selection,” Energy, vol. 169, pp. 263–276, 2019. DOI: 10.1016/j.energy.2018.12.014.
- P. O. Oviroh and T. C. Jen, “The energy cost analysis of hybrid systems and diesel generators in powering selected base transceiver station locations in Nigeria,” Energies, vol. 11, no. 3, pp. 687–9, 2018. DOI: 10.3390/en11030687.
- A. Ushakova and S. J. Mikhaylov, “Big data to the rescue? Challenges in analyzing granular household electricity consumption in the United Kingdom,” Energy Res. Soc. Sci., vol. 64, pp. 101428, 2020. DOI: 10.1016/j.erss.2020.10142.