A Novel Clustering-based Forecast Framework: The Clusters with Competing Configurations Approach

Accurate aggregate (total) short-term load forecasting of Smart Homes (SHs) is essential in planning and management of power utilities. The baseline approach consists of simply designing and training predictors for the aggregated consumption data. Nevertheless, better performance can be achieved by using a clustering-based forecasting strategy. In such strategy, the SHs are grouped according to some metric and the forecast of each group's total consumption are summed to reach the forecast of aggregate consumption of all SHs. Although the idea is simple, its implementation requires fine-detailed steps. This paper proposes a novel clustering-based aggregate-level forecast framework, so called Clusters with Competing Configurations (CwCC) approach and then compares its performance to the baseline strategy, namely Clusters with the Same Configurations (CwSC) approach. The Configurations in the name refers to the configurations of ARIMA, Multi-Layer Perceptron (MLP), and Long Short-Term Memory (LSTM) forecasting methods, which the CwCC approach uses. We test the CwCC approach on Smart Grid Smart City Dataset. The results show that better performance can be achieved using the CwCC approach for each of the three forecast methods, and LSTM outperforms other methods in each scenario.

Keywords:

clustering, deep neural networks short-term load forecasting, smart grid,

PDF

___

G. Pau, M. Collotta, A. Ruano, and J. Qin, “Smart Home Energy Management,” Energies (Basel), vol. 10, no. 3, p. 382, Mar. 2017, doi: 10.3390/en10030382.
A. Iranpour Mobarakeh, R. Sadeghi, H. Saghafi esfahani, and M. Delshad, “Techno-economic energy management of micro-grid in the presence of distributed generation sources based on demand response programs,” International Journal of Electrical Power & Energy Systems, vol. 141, p. 108169, Oct. 2022, doi: 10.1016/j.ijepes.2022.108169.
A. Shewale, A. Mokhade, N. Funde, and N. D. Bokde, “A Survey of Efficient Demand-Side Management Techniques for the Residential Appliance Scheduling Problem in Smart Homes,” Energies (Basel), vol. 15, no. 8, p. 2863, Apr. 2022, doi: 10.3390/en15082863.
A. Kahraman, O. Bulut, E. Biyik, C. Guzelis, and G. Demirkiran, “Stochastic Microgrid Control Problems: Effects of Load Distribution and Planning Horizon,” in 2019 Innovations in Intelligent Systems and Applications Conference (ASYU), IEEE, Oct. 2019, pp. 1–6. doi: 10.1109/ASYU48272.2019.8946439.
F. Agner, “Creating Electrical Load Profiles Through Time Series Clustering,” 2019.
S. Yilmaz, J. Chambers, and M. K. Patel, “Comparison of clustering approaches for domestic electricity load profile characterisation - Implications for demand side management,” Energy, vol. 180, pp. 665–677, Aug. 2019, doi: 10.1016/j.energy.2019.05.124.
K. Zhou, S. Yang, and Z. Shao, “Household monthly electricity consumption pattern mining: A fuzzy clustering-based model and a case study,” J Clean Prod, vol. 141, pp. 900–908, Jan. 2017, doi: 10.1016/j.jclepro.2016.09.165.
G. Le Ray and P. Pinson, “Online adaptive clustering algorithm for load profiling,” Sustainable Energy, Grids and Networks, vol. 17, Mar. 2019, doi: 10.1016/j.segan.2018.100181.
S. Lin, F. Li, E. Tian, Y. Fu, and D. Li, “Clustering load profiles for demand response applications,” IEEE Trans Smart Grid, vol. 10, no. 2, pp. 1599–1607, Mar. 2019, doi: 10.1109/TSG.2017.2773573.
E. Mele, C. Elias, and A. Ktena, “Electricity use profiling and forecasting at microgrid level,” 2018.
M. Alhussein, K. Aurangzeb, and S. I. Haider, “Hybrid CNN-LSTM Model for Short-Term Individual Household Load Forecasting,” IEEE Access, vol. 8, pp. 180544–180557, 2020, doi: 10.1109/ACCESS.2020.3028281.
Y. Yang, W. Li, T. A. Gulliver, and S. Li, “Bayesian Deep Learning-Based Probabilistic Load Forecasting in Smart Grids,” IEEE Trans Industr Inform, vol. 16, no. 7, pp. 4703–4713, Jul. 2020, doi: 10.1109/TII.2019.2942353.
C. Alzate and M. Sinn, “Improved electricity load forecasting via kernel spectral clustering of smart meters,” Proceedings - IEEE International Conference on Data Mining, ICDM, pp. 943–948, 2013, doi: 10.1109/ICDM.2013.144.
T. K. Wijaya, M. Vasirani, S. Humeau, and K. Aberer, “Cluster-based aggregate forecasting for residential electricity demand using smart meter data,” in Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015, Institute of Electrical and Electronics Engineers Inc., Dec. 2015, pp. 879–887. doi: 10.1109/BigData.2015.7363836.
A. Shahzadeh, A. Khosravi, and S. Nahavandi, “Improving load forecast accuracy by clustering consumers using smart meter data,” in 2015 International Joint Conference on Neural Networks (IJCNN), IEEE, Jul. 2015, pp. 1–7. doi: 10.1109/IJCNN.2015.7280393.
S. Bandyopadhyay, T. Ganu, H. Khadilkar, and V. Arya, “Individual and aggregate electrical load forecasting: One for all and all for one,” in e-Energy 2015 - Proceedings of the 2015 ACM 6th International Conference on Future Energy Systems, Association for Computing Machinery, Inc, Jul. 2015, pp. 121–130. doi: 10.1145/2768510.2768539.
F. Fahiman, S. M. Erfani, S. Rajasegarar, M. Palaniswami, and C. Leckie, “Improving load forecasting based on deep learning and K-shape clustering,” in Proceedings of the International Joint Conference on Neural Networks, Institute of Electrical and Electronics Engineers Inc., Jun. 2017, pp. 4134–4141. doi: 10.1109/IJCNN.2017.7966378.
T. Jarabek, P. Laurinec, and M. Lucka, “Energy load forecast using S2S deep neural networks with k-Shape clustering,” in 2017 IEEE 14th International Scientific Conference on Informatics, IEEE, Nov. 2017, pp. 140–145. doi: 10.1109/INFORMATICS.2017.8327236.
A. Cini, S. Lukovic, and C. Alippi, “Cluster-based Aggregate Load Forecasting with Deep Neural Networks,” in 2020 International Joint Conference on Neural Networks (IJCNN), IEEE, Jul. 2020, pp. 1–8. doi: 10.1109/IJCNN48605.2020.9207503.
Y. Wang, Q. Chen, T. Hong, and C. Kang, “Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges,” IEEE Trans Smart Grid, vol. 10, no. 3, pp. 3125–3148, May 2019, doi: 10.1109/TSG.2018.2818167.
S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Comput, vol. 9, no. 8, pp. 1735–1780, Nov. 1997, doi: 10.1162/neco.1997.9.8.1735.
Australian Government, “Smart Grid Smart City (SGSC). Customer trial data,” https://data.gov.au/dataset/ds-dga-4e21dea3-9b87-4610-94c7-15a8a77907ef/details, May 20, 2022.
O. Motlagh, A. Berry, and L. O’Neil, “Clustering of residential electricity customers using load time series,” Appl Energy, vol. 237, pp. 11–24, Mar. 2019, doi: 10.1016/j.apenergy.2018.12.063.