Abstract
The short- and long-term forecasting of the grid electrical energy of a province or region is important for the management of the conventional electricity transmission and distribution network. Nowadays, the amount of electrical energy, which each residential building has taken from the grid, has gained importance within the scope of smart grids. Residential buildings that take electricity from the smart grid can generate electricity with alternative energy sources such as solar energy. Besides generating its energy these buildings can also take electricity from the electricity grid. In other words, they can use both the energy that they generate and the energy that they receive from the electricity grid. This bi-directional energy flow increases system complexity, and the grid system become more dynamic. Therefore, estimating the electrical load of such a system is also more difficult than the conventional grid system. In this study, the recurrent linear regression method (R-LR), which is based on the linear regression, was proposed. In order to test and validate the proposed approach, the Sundance dataset, which is shared in the U mass trace repository according to smart project was used. To confirm the success of the proposed method, the linear regression (LR), and the extreme learning machine (ELM) methods were used in each of the 59 different datasets. Obtained results, which were applied to 59 different residential buildings smart meter datasets, showed that lower root means square error and corrected symmetric mean absolute percentage error, which was proposed in this paper in order to eliminate zero dividing errors, values were achieved by R-LR compared to LR and ELM. It was found that the proposed R-LR provides better results in modeling dynamic systems and gives good results in forecasting analysis with a time-series dataset.
Similar content being viewed by others
References
Galli S, Scaglione A, Wang Z (2011) For the grid and through the grid: the role of power line communications in the smart grid. Proc IEEE 99(6):998–1006
Marszal AJ, Heiselberg P, Bourrelle JS, Musall E, Voss K, Sartori I, Napolitano A (2011) Zero energy building—a review of definitions and calculation methodologies. Lancet 43:971–976
Bollen M, Das R, Djokic S, Ciufo P, Meyer J, Rönnberg S, Zavoda F (2016) Power quality concerns in implementing smart distribution-grid applications. IEEE Trans Smart Grid PP(99):1–5
Eid C, Codani P, Perez Y, Reneses J, Hakvoort R (2016) Managing electric flexibility from distributed energy resources: a review of incentives for market design. Renew Sustain Energy Rev 64:237–242
Gajowniczek K, Zabkowski T (2017) Two-stage electricity demand modelling using machine learning algorithms. Energies 10(10):1547–1560
Haida T, Muto S (1994) Regression based peak load forecasting using a transformation technique. IEEE Trans Power Syst 9(4):1788–1794
Guan C, Luh PB, Michel LD, Wang Y, Friedland PB (2013) Very short-term load forecasting: wavelet neural networks with data pre-filtering. IEEE Trans Power Syst 28(1):30–41
Cervone G, Harding L, Alessandrini S, Monache L (2017) Short-term photovoltaic power forecasting using artificial neural network and an analog ensemble. Renew Energy 108:274–280
Dong B, Li Z, Rahman SM, Vega R (2016) A hybrid model approach for forecasting future residential electricity consumption. Energy Build 117:341–347
Osman ZH, Awad ML, Mahmoud TK (2009) Neural network based approach for short-term load forecasting. In: Power systems conference and exposition, pp 1–8
Ertugrul ÖF (2016) Forecasting electricity load by a novel recurrent extreme learning machines approach. Int J Electr Power Energy Syst 78:429–435
Rahman A, Srikumar V, Smith AD (2018) Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks. Appl Energy 212:372–385
Kong W, Dong ZY, Jia Y, Hill DJ, Xu Y, Zhang Y (2017) Short term residential load forecasting based on LSTM recurrent network. IEEE Trans Power Syst 10(1):841–851
Chitsaz H, Shaker H, Zareipour H, Wood D, Amjady N (2015) Short-term electricity load forecasting of buildings in microgrids. Energy Build 99:50–60
King ML (2018) Testing for autocorrelation in linear regression models: a survey. In: Specification analysis in the linear model, chapter 3. Routledge, London, pp 19–73
Borchani H, Varando G, Bielza C, Larrañaga P (2015) A survey on multi-output regression. Wiley Interdiscip Rev Data Min Knowl Discov 5(5):216–233
Voyant C, Notton G, Kalogirou S, Nivet ML, Paoli C, Motte F, Fouilloy A (2017) Machine learning methods for solar radiation forecasting: a review. Renew Energy 105:569–582
Chen D, Irwin D (2017) Sundance: Black-Box behind the meter solar disaggregation. In: e-Energy’17 Proceedings of the 8 international conference on future energy system, Shatin, Honkong, pp 45–50
Bianco V, Manca O, Nardini S (2009) Electricity consumption forecasting in Italy using linear regression models. Energy 34(9):1413–1421
Zheng J, Xu C, Zhang Z, Li X (2017) Electric load forecasting in smart grids using long-short-term-memory based recurrent neural network. In: IEEE 2017 51st annual conference on information sciences and systems (CISS), pp 1–6
Kaya Y, Ertuğrul ÖF, Tekin R (2014) An expert spam detection system based on extreme learning machine. Comput Sci Appl 1(2):132–137
Kasun L, Zhou H, Huang G, Vong C (2013) Representational learning with ELMs for big data. Intell Syst IEEE 28(6):31–34
Wang Y, Liu M, Bao Z, Zhang S (2018) Short-term load forecasting with multi-source data using gated recurrent unit neural networks. Energies 11(5):1138
Kaya Y, Kayci L, Tekin R, Ertuğrul ÖF (2014) Evaluation of texture features for automatic detecting butterfly species using extreme learning machine. J Exp Theor Artif Intell 26(2):267–281
Jones MT (2017) Recurrent neural Networks deep dive. https://developer.ibm.com/articles/cc-cognitive-recurrent-neural-networks
Shcherbakov MV, Brebels A, Shcherbakova NL, Tyukov AP, Janovsky TA, Kamaev VAE (2013) A survey of forecast error measures. World Appl Sci J 24(24):171–176
Tamarkin T (2016) Smart energy-smart home cloud based consumer home automation for electricity, water and gas management, EnergyCite LTD, pp 1–3. https://energycite.com/smart-energy-smart-home-2/
Funding
This study was not funded.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Ertuğrul, Ö.F., Tekin, H. & Tekin, R. A novel regression method in forecasting short-term grid electricity load in buildings that were connected to the smart grid. Electr Eng 103, 717–728 (2021). https://doi.org/10.1007/s00202-020-01114-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00202-020-01114-3