Boost short-term load forecasts with synthetic data from transferred latent space information
Tóm tắt
Sustainable energy systems are characterised by an increased integration of renewable energy sources, which magnifies the fluctuations in energy supply. Methods to to cope with these magnified fluctuations, such as load shifting, typically require accurate short-term load forecasts. Although numerous machine learning models have been developed to improve short-term load forecasting (STLF), these models often require large amounts of training data. Unfortunately, such data is usually not available, for example, due to new users or privacy concerns. Therefore, obtaining accurate short-term load forecasts with little data is a major challenge. The present paper thus proposes the latent space-based forecast enhancer (LSFE), a method which combines transfer learning and data augmentation to enhance STLF when training data is limited. The LSFE first trains a generative model on source data similar to the target data before using the latent space data representation of the target data to generate seed noise. Finally, we use this seed noise to generate synthetic data, which we combine with real data to enhance STLF. We evaluate the LSFE on real-world electricity data by examining the influence of its components, analysing its influence on obtained forecasts, and comparing its performance to benchmark models. We show that the Latent Space-based Forecast Enhancer is generally capable of improving the forecast accuracy and thus helps to successfully meet the challenge of limited available training data.
Tài liệu tham khảo
Ardizzone L, Lüth C, Kruse J, Rother C, Köthe U (2019) Guided image generation with conditional invertible neural networks. arXiv:1907.02392
Alrawi O, Bayram IS, Al-Ghamdi SG, Koc M (2019) High-resolution household load profiling and evaluation of rooftop PV systems in selected houses in Qatar. Energies 12(20):3876
Chollet F et al. (2015) Keras . https://keras.io
Do H, Cetin KS (2018) Residential building energy consumption: a review of energy data availability, characteristics, and energy performance prediction methods. Curr Sustain/Renew Energy Rep 5(1):76–85
Dua D, Graff C (2019) UCI machine learning repository. http://archive.ics.uci.edu/ml
Fan C, Chen M, Wang X, Wang J, Huang B (2021) A review on data preprocessing techniques toward efficient and reliable knowledge discovery from building operational data. Front Energy Res 9:652801
Fan C, Chen M, Tang R, Wang J (2022) A novel deep generative modeling-based data augmentation strategy for improving short-term building energy predictions. Build Simul 15(2):197–211
Gomez-Rosero S, Capretz MAM, Mir S (2021) Transfer learning by similarity centred architecture evolution for multiple residential load forecasting. Smart Cities 4(1):217–240
González Ordiano JÁ, Waczowicz S, Hagenmeyer V, Mikut R (2018) Energy forecasting tools and services. Wiley Interdiscipl Rev Data Mining Knowl Discov 8(2):1235
Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press, Cambridge
Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning, 2nd edn. Springer, New York
Heidrich B, Bartschat A, Turowski M, Neumann O, Phipps K, Meisenbacher S, Schmieder K, Ludwig N, Mikut R, Hagenmeyer V (2021) pyWATTS: Python workflow automation tool for time series. arXiv:2106.10157
Heidrich B, Turowski M, Phipps K, Schmieder K, Süß W, Mikut R, Hagenmeyer V (2022) Controlling non-stationarity and periodicities in time series generation using conditional invertible neural networks. Appl Intell
Hinton G, Srivastava N, Swersky K (2012) Neural networks for machine learning lecture: lecture 6a overview of mini-batch gradient descent. http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf
Hippert HS, Pedreira CE, Souza RC (2001) Neural networks for short-term load forecasting: a review and evaluation. IEEE Trans Power Syst 16(1):44–55
Hooshmand A, Sharma R (2019) Energy predictive models with limited data using transfer learning. In: The Tenth ACM International Conference on Future Energy Systems (e-Energy 2019), pp. 12–16
Kingma DP, Ba JL (2015) Adam: a method for stochastic optimization. In: 3rd International Conference on Learning Representations (ICLR 2015)
Kingma DP, Dhariwal P (2018) Glow: generative flow with invertible 1x1 convolutions. In: Bengio S, Wallach H, Larochelle H, Grauman K, Cesa-Bianchi N, Garnett R (eds) Advances in neural information processing systems, vol. 31, pp. 10215–10224
Kroposki B, Johnson B, Zhang Y, Gevorgian V, Denholm P, Hodge B-M, Hannegan B (2017) Achieving a 100% renewable grid: operating electric power systems with extremely high levels of variable renewable energy. IEEE Power Energy Mag 15(2):61–73
Li A, Xiao F, Fan C, Hu M (2021) Development of an ANN-based building energy model for information-poor buildings using transfer learning. Build Simul 14:89–101
Lin W, Wu D (2021) Residential electric load forecasting via attentive transfer of graph neural networks. In: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI-21), pp. 2716–2722
Maalej A, Rebai C (2021) Sensor data augmentation strategy for load forecasting in smart grid context. In: 2021 18th International Multi-Conference on Systems, Signals & Devices (SSD), pp. 979–983
Mabel MC, Fernandez E (2008) Analysis of wind power generation and prediction using ANN: a case study. Renew Energy 33(5):986–992
Moon J, Kim J, Kang P, Hwang E (2020) Solving the cold-start problem in short-term load forecasting using tree-based methods. Energies 13(4):886
Ozer I, Efe SB, Ozbay H (2021) A combined deep learning application for short term load forecasting. Alex Eng J 60(4):3807–3818
Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, Desmaison A, Kopf A, Yang E, DeVito Z, Raison M, Tejani A, Chilamkurthy S, Steiner B, Fang L, Bai J, Chintala S (2019) Pytorch: an imperative style, high-performance deep learning library. In: Wallach H, Larochelle H, Beygelzimer A, D’Alché-Buc F, Fox E, Garnett R (eds) Advances in neural information processing systems, vol 32, pp. 8024–8035
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830
Ribeiro M, Grolinger K, ElYamany HF, Higashino WA, Capretz MAM (2018) Transfer learning with seasonal and trend adjustment for cross-building energy forecasting. Energy Build 165:352–363
Rodrigues F, Trindade A (2018) Load forecasting through functional clustering and ensemble learning. Knowl Inf Syst 57(1):229–244
Sohn K, Yan X, Lee H (2015) Learning structured output representation using deep conditional generative models. In: Cortes C, Lawrence N, Lee D, Sugiyama M, Garnett R (eds) Advances in neural information processing systems, vol. 28, pp. 3483–3491
Tian Y, Sehovac L, Grolinger K (2019) Similarity-based chained transfer learning for energy forecasting with big data. IEEE Access 7:139895–139908
Upadhaya D, Thakur R, Singh NK (2019) A systematic review on the methods of short term load forecasting. In: 2019 2nd International Conference on Power Energy, Environment and Intelligent Control (PEEIC), pp. 6–11
van der Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9(86):2579–2605
Voß M, Bender-Saebelkampf C, Albayrak S (2018) Residential short-term load forecasting using convolutional neural networks. In: 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)
Wu L, Shahidehpour M (2010) A hybrid model for day-ahead price forecasting. IEEE Trans Power Syst 25(3):1519–1530
Xu X, Meng Z (2020) A hybrid transfer learning model for short-term electric load forecasting. Electr Eng 102(3):1371–1381
Yona A, Senjyu T, Saber AY, Funabashi T, Sekine H, Kim CH (2008) Application of neural network to 24-hour-ahead generating power forecasting for pv system. In: 2008 IEEE Power and Energy Society General Meeting—Conversion and Delivery of Electrical Energy in the 21st Century
Zhang Y, Luo G (2015) Short term power load prediction with knowledge transfer. Inf Syst 53:161–169
Zhou D, Ma S, Hao J, Han D, Huang D, Yan S, Li T (2020) An electricity load forecasting model for Integrated Energy System based on BiGAN and transfer learning. Energy Rep 6:3446–3461