Residential Electricity Load Scenario Prediction Based on Transferable Flow Generation Model
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Tang X, Chen H, Xiang W et al (2022) Short-term load forecasting using channel and temporal attention based temporal convolutional network. Electr Power Syst Res 205:107761–107774
Kwon BS, Park RJ, Song KB (2020) Short-term load forecasting based on deep neural networks using LSTM layer. J. Electr. Eng. Technol 15:1501–1509
Ahmad A, Javaid N, Guizani M, Alrajeh N, Khan ZA (2017) An accurate and fast converging short-term load forecasting model for industrial applications in a smart grid. IEEE Trans Ind Inform 13(5):2587–2596
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
Jufri FH, Oh S, Jung J (2019) Day-ahead system marginal price forecasting using artificial neural network and similar-days information. J Electr Eng Technol 14:561–568
Ruxue L, Shumin L, Miaona Y et al (2021) Load forecasting based on weighted grey relational degree and improved ABC-SVM. J Electr Eng Technol 16:2191–2200
Hong T, Wang P, Willis HL (2011) A Naïve multiple linear regression benchmark for short term load forecasting. In: 2011 IEEE power and energy society general meeting, pp 1–6
Lemos-Vinasco J, Bacher P, Møller JK (2021) Probabilistic load forecasting considering temporal correlation: online models for the prediction of households’ electrical load. Appl Energy 303:117594–117604
Chen Y, Wang Y, Kirschen D, Zhang B (2018) Model-free renewable scenario generation using generative adversarial networks. IEEE Trans Power Syst 33(3):3265–3275
Chen Y, Wang X, Zhang B (2018) An unsupervised deep learning approach for scenario forecasts. In: 2018 power systems computation conference (PSCC), pp 1–7
Ge L, Liao W, Wang S, Bak-Jensen B, Pillai JR (2020) Modeling daily load profiles of distribution network for scenario generation using flow-based generative network. IEEE Access 8:77587–77597
Hyndman RJ, Athanasopoulos G (2014) Forecasting: principles and practice. OTexts
Huang N, Wang W, Cai G (2020) Optimal configuration planning of multi-energy microgrid based on deep joint generation of source-load-temperature scenarios. CSEE J Power Energy Syst
Gu Y, Chen Q, Liu K, Xie L, Kang C (2019) GAN-based model for residential load generation considering typical consumption patterns. In: 2019 IEEE power and energy society innovative smart grid technologies conference (ISGT), pp 1–5
Zhang L, Zhang B (2019) Scenario forecasting of residential load profiles. IEEE J Sel Areas Commun 38(1):84–95
Zhang YL, Luo GM (2015) Short term power load prediction with knowledge transfer. Inf Syst 53:161–169
Zeng P, Sheng C, Jin M (2019) A learning framework based on weighted knowledge transfer for holiday load forecasting. J Mod Power Syst Clean Energy 7(2):329–339
Zhou D, Ma S, Hao J et al (2020) An electricity load forecasting model for integrated energy system based on BiGAN and transfer learning. Energy Rep 6:3446–3461
Valenzuela C, Diego SF, Gómez V (2021) Expression transfer using flow-based generative models. In: IEEE/CVF conference on computer vision and pattern recognition workshops (CVPRW), pp 1023–1031
Dumas J, Wehenkel A, Lanaspeze D et al (2022) A deep generative model for probabilistic energy forecasting in power systems: normalizing flows. Appl Energy 305:117871–117892
Kingma DP, Dhariwal P (2018) Glow: generative flow with invertible 1x1 convolutions. Adv Neural Inf Process Syst 10:236–245
P. S. Inc. https://dataport.pecanstreet.org.