Estimating Demand Flexibility Using Siamese LSTM Neural Networks

• URL: http://arxiv.org/abs/2109.01258v1
• Date: Fri, 3 Sep 2021 00:59:27 GMT
• Title: Estimating Demand Flexibility Using Siamese LSTM Neural Networks
• Authors: Guangchun Ruan, Daniel S. Kirschen, Haiwang Zhong, Qing Xia, Chongqing Kang
• Abstract summary: We quantify demand flexibility using an efficient tool called time-varying elasticity, whose value may change depending on the prices and decision dynamics. Recent empirical evidences have highlighted some abnormal features when studying demand flexibility, such as delayed responses and vanishing elasticities after price spikes. This paper proposes a model-free methodology to automatically and accurately derive the optimal estimation pattern.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: There is an opportunity in modern power systems to explore the demand flexibility by incentivizing consumers with dynamic prices. In this paper, we quantify demand flexibility using an efficient tool called time-varying elasticity, whose value may change depending on the prices and decision dynamics. This tool is particularly useful for evaluating the demand response potential and system reliability. Recent empirical evidences have highlighted some abnormal features when studying demand flexibility, such as delayed responses and vanishing elasticities after price spikes. Existing methods fail to capture these complicated features because they heavily rely on some predefined (often over-simplified) regression expressions. Instead, this paper proposes a model-free methodology to automatically and accurately derive the optimal estimation pattern. We further develop a two-stage estimation process with Siamese long short-term memory (LSTM) networks. Here, a LSTM network encodes the price response, while the other network estimates the time-varying elasticities. In the case study, the proposed framework and models are validated to achieve higher overall estimation accuracy and better description for various abnormal features when compared with the state-of-the-art methods.

Related papers

• Application of Deep Learning for Factor Timing in Asset Management [21.212548040046133]
  More flexible models have better performance in explaining the variance in factor premium of the unseen period. For flexible models like neural networks, the optimal weights based on their prediction tend to be unstable. We verify that tilting down the rebalance frequency according to the historical optimal rebalancing scheme can help reduce the transaction costs.
  arXiv  Detail & Related papers  (2024-04-27T21:57:17Z)
• XRMDN: An Extended Recurrent Mixture Density Network for Short-Term Probabilistic Rider Demand Forecasting with High Volatility [16.047461063459846]
  We propose an Extended Recurrent Mixture Density Network (XRMDN) to process demand residuals and variance through correlated modules. XRMDN adeptly captures demand trends, thus significantly enhancing forecasting precision, particularly in high-volatility scenarios.
  arXiv  Detail & Related papers  (2023-10-15T14:18:42Z)
• STEF-DHNet: Spatiotemporal External Factors Based Deep Hybrid Network for Enhanced Long-Term Taxi Demand Prediction [16.07685260834701]
  This paper introduces STEF-DHNet, a demand prediction model that integrates external features astemporal information. It is evaluated using a long-term performance metric called the rolling error, which assesses its ability to maintain high accuracy over long periods without retraining. The results show that STEF-DHNet outperforms existing state-of-the-art methods on three diverse datasets.
  arXiv  Detail & Related papers  (2023-06-26T07:37:50Z)
• Value function estimation using conditional diffusion models for control [62.27184818047923]
  We propose a simple algorithm called Diffused Value Function (DVF) It learns a joint multi-step model of the environment-robot interaction dynamics using a diffusion model. We show how DVF can be used to efficiently capture the state visitation measure for multiple controllers.
  arXiv  Detail & Related papers  (2023-06-09T18:40:55Z)
• Structured Dynamic Pricing: Optimal Regret in a Global Shrinkage Model [50.06663781566795]
  We consider a dynamic model with the consumers' preferences as well as price sensitivity varying over time. We measure the performance of a dynamic pricing policy via regret, which is the expected revenue loss compared to a clairvoyant that knows the sequence of model parameters in advance. Our regret analysis results not only demonstrate optimality of the proposed policy but also show that for policy planning it is essential to incorporate available structural information.
  arXiv  Detail & Related papers  (2023-03-28T00:23:23Z)
• Augmented Bilinear Network for Incremental Multi-Stock Time-Series Classification [83.23129279407271]
  We propose a method to efficiently retain the knowledge available in a neural network pre-trained on a set of securities. In our method, the prior knowledge encoded in a pre-trained neural network is maintained by keeping existing connections fixed. This knowledge is adjusted for the new securities by a set of augmented connections, which are optimized using the new data.
  arXiv  Detail & Related papers  (2022-07-23T18:54:10Z)
• Demand Response Method Considering Multiple Types of Flexible Loads in Industrial Parks [14.160676017436167]
  A new demand response method considering multiple flexible loads is proposed in this paper. The proposed method is significantly better than the existing technologies in reducing load modeling deviation and improving the responsiveness of park loads.
  arXiv  Detail & Related papers  (2022-05-24T03:18:06Z)
• Bilinear Input Normalization for Neural Networks in Financial Forecasting [101.89872650510074]
  We propose a novel data-driven normalization method for deep neural networks that handle high-frequency financial time-series. The proposed normalization scheme takes into account the bimodal characteristic of financial time-series. Our experiments, conducted with state-of-the-arts neural networks and high-frequency data, show significant improvements over other normalization techniques.
  arXiv  Detail & Related papers  (2021-09-01T07:52:03Z)
• Predicting Inflation with Recurrent Neural Networks [0.0]
  This paper applies a recurrent neural network, the LSTM, to forecast inflation. Results from an exercise with US data indicate that the estimated neural nets present competitive, but not outstanding, performance against common benchmarks.
  arXiv  Detail & Related papers  (2021-04-08T13:19:26Z)
• Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encoder [59.69303945834122]
  We present a Smoothness-Inducing Sequential Variational Auto-Encoder (SISVAE) model for robust estimation and anomaly detection of time series. Our model parameterizes mean and variance for each time-stamp with flexible neural networks. We show the effectiveness of our model on both synthetic datasets and public real-world benchmarks.
  arXiv  Detail & Related papers  (2021-02-02T06:15:15Z)
• AdaS: Adaptive Scheduling of Stochastic Gradients [50.80697760166045]
  We introduce the notions of textit"knowledge gain" and textit"mapping condition" and propose a new algorithm called Adaptive Scheduling (AdaS) Experimentation reveals that, using the derived metrics, AdaS exhibits: (a) faster convergence and superior generalization over existing adaptive learning methods; and (b) lack of dependence on a validation set to determine when to stop training.
  arXiv  Detail & Related papers  (2020-06-11T16:36:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.