Book ChapterDOI
Short-Term Load Forecasting Based on CNN-BiLSTM with Bayesian Optimization and Attention Mechanism
Kai Miao,Qiang Hua,Huifeng Shi +2 more
- pp 116-128
TLDR
In this article, a short-term load forecasting based on Convolutional Neural Networks and Bidirectional Long Short-Term Memory (CNN-BiLSTM) with Bayesian Optimization (BO) and Attention Mechanism (AM) was proposed.Abstract:
Short-term power load forecasting is quite vital in maintaining the balance between power production and power consumption of the power grid. Prediction accuracy not only affects the power grid construction, but also influences the economic development of the power grid. This paper proposes a short-term load forecasting based on Convolutional Neural Networks and Bidirectional Long Short-Term Memory (CNN-BiLSTM) with Bayesian Optimization (BO) and Attention Mechanism (AM). The BiLSTM is good at time series forecasting, and the Attention Mechanism can help the model to focus on the important part of the BiLSTM output. In order to make the forecasting performance of the model as good as possible, the Bayesian Optimization is used to tune the hyperparameters of the model. The input of the model is history load, time slot, and meteorological factors. In order to eliminate the seasonal influence, the data set is divided into four subsets with respect to four seasons. The performance of the proposed model is compared with other forecasting models by MAE, RMSE, MAPE, and \(R^2\) score. The experiment results show that the proposed model fits the actual values best and has the best forecasting performance among the contrast models.read more
Citations
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Journal ArticleDOI
Short-Term Power Load Forecasting Based on SAPSO-CNN-LSTM Model considering Autocorrelated Errors
TL;DR: This study proposes a new power load forecasting method that uses the simulated annealing particle swarm optimization (SAPSO) algorithm to optimize the hyperparameters and shows that the prediction accuracy of the proposed model is higher compared with LSTM, CNN-L STM, and other models.
Journal ArticleDOI
Energy Demand Forecasting and Optimizing Electric Systems for Developing Countries
TL;DR: In this article , the authors review the current pragmatic methods for forecasting the future load demands from minutes to years ahead in developing countries, following the Preferred Reporting Items for Systematic review and Meta-Analysis Protocols (PRISMA-P).
Proceedings ArticleDOI
Short-Term Aggregated Residential Load Forecasting using BiLSTM and CNN-BiLSTM
TL;DR: In this article , the authors investigated the capabilities of a bidirectional long short-term memory (BiLSTM) and a convolutional neural network-based Bi-STM to provide a day ahead (24 hr.) forecasting at an hourly resolution while minimizing the root mean squared error (RMSE) between the actual and predicted load demand.
Journal ArticleDOI
Research on Electric Load Forecasting and User Benefit Maximization Under Demand-Side Response
TL;DR: In this paper , the real-time changes of demand-side response factors are accurately considered, and an attention mechanism is introduced to automatically assign the corresponding weights to the hidden layer states of BiLSTM.
Book ChapterDOI
Energy Consumption Prediction for Multi-functional Buildings Using Convolutional Bidirectional Recurrent Neural Networks
TL;DR: In this paper, a Conv-BiLSTM hybrid architecture is proposed to improve building energy consumption reconstruction of a new multi-functional building type, and experiments indicate that using the proposed hybrid architecture results in improved prediction accuracy for two case multifunction buildings in ultra-short-term to short term energy use modelling, with a score ranging between 0.81 to 0.94.
References
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Long short-term memory
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Journal ArticleDOI
Short-term load forecasting via ARMA model identification including non-Gaussian process considerations
Shyh-Jier Huang,Kuang-Rong Shih +1 more
TL;DR: The concept of cumulant and bispectrum are embedded into the ARMA model in order to facilitate Gaussian and non-Gaussian process considerations, and with embodiment of a Gaussianity verification procedure, the forecasted model is identified more appropriately.
Journal ArticleDOI
The Time Series Approach to Short Term Load Forecasting
Martin T. Hagan,Suzanne M. Behr +1 more
TL;DR: It is shown than Box and Jenkins time series models, in particular, are well suited to this application and one of the drawbacks of these models is the inability to accurately represent the nonlinear relationship between load and temperature.