Short-term Load Forecasting with Distributed Long Short-Term Memory

• URL: http://arxiv.org/abs/2208.01147v1
• Date: Mon, 1 Aug 2022 21:40:16 GMT
• Title: Short-term Load Forecasting with Distributed Long Short-Term Memory
• Authors: Yi Dong, Yang Chen, Xingyu Zhao, Xiaowei Huang
• Abstract summary: This paper presents a fully distributed short-term load forecasting framework based on a consensus algorithm and Long Short-Term Memory (LSTM) Specifically, a fully distributed learning framework is exploited for distributed training, and a consensus technique is applied to meet confidential privacy. Case studies show that the proposed method has comparable performance with centralised methods regarding the accuracy.
• Score: 10.99865735713831
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the employment of smart meters, massive data on consumer behaviour can be collected by retailers. From the collected data, the retailers may obtain the household profile information and implement demand response. While retailers prefer to acquire a model as accurate as possible among different customers, there are two major challenges. First, different retailers in the retail market do not share their consumer's electricity consumption data as these data are regarded as their assets, which has led to the problem of data island. Second, the electricity load data are highly heterogeneous since different retailers may serve various consumers. To this end, a fully distributed short-term load forecasting framework based on a consensus algorithm and Long Short-Term Memory (LSTM) is proposed, which may protect the customer's privacy and satisfy the accurate load forecasting requirement. Specifically, a fully distributed learning framework is exploited for distributed training, and a consensus technique is applied to meet confidential privacy. Case studies show that the proposed method has comparable performance with centralised methods regarding the accuracy, but the proposed method shows advantages in training speed and data privacy.

Related papers

• Pseudo-Probability Unlearning: Towards Efficient and Privacy-Preserving Machine Unlearning [59.29849532966454]
  We propose PseudoProbability Unlearning (PPU), a novel method that enables models to forget data to adhere to privacy-preserving manner. Our method achieves over 20% improvements in forgetting error compared to the state-of-the-art.
  arXiv  Detail & Related papers  (2024-11-04T21:27:06Z)
• F-FOMAML: GNN-Enhanced Meta-Learning for Peak Period Demand Forecasting with Proxy Data [65.6499834212641]
  We formulate the demand prediction as a meta-learning problem and develop the Feature-based First-Order Model-Agnostic Meta-Learning (F-FOMAML) algorithm. By considering domain similarities through task-specific metadata, our model improved generalization, where the excess risk decreases as the number of training tasks increases. Compared to existing state-of-the-art models, our method demonstrates a notable improvement in demand prediction accuracy, reducing the Mean Absolute Error by 26.24% on an internal vending machine dataset and by 1.04% on the publicly accessible JD.com dataset.
  arXiv  Detail & Related papers  (2024-06-23T21:28:50Z)
• Personalized Federated Learning under Mixture of Distributions [98.25444470990107]
  We propose a novel approach to Personalized Federated Learning (PFL), which utilizes Gaussian mixture models (GMM) to fit the input data distributions across diverse clients. FedGMM possesses an additional advantage of adapting to new clients with minimal overhead, and it also enables uncertainty quantification. Empirical evaluations on synthetic and benchmark datasets demonstrate the superior performance of our method in both PFL classification and novel sample detection.
  arXiv  Detail & Related papers  (2023-05-01T20:04:46Z)
• Private Set Generation with Discriminative Information [63.851085173614]
  Differentially private data generation is a promising solution to the data privacy challenge. Existing private generative models are struggling with the utility of synthetic samples. We introduce a simple yet effective method that greatly improves the sample utility of state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-11-07T10:02:55Z)
• Approaching sales forecasting using recurrent neural networks and transformers [57.43518732385863]
  We develop three alternatives to tackle the problem of forecasting the customer sales at day/store/item level using deep learning techniques. Our empirical results show how good performance can be achieved by using a simple sequence to sequence architecture with minimal data preprocessing effort. The proposed solution achieves a RMSLE of around 0.54, which is competitive with other more specific solutions to the problem proposed in the Kaggle competition.
  arXiv  Detail & Related papers  (2022-04-16T12:03:52Z)
• FedREP: Towards Horizontal Federated Load Forecasting for Retail Energy Providers [1.1254693939127909]
  We propose a novel horizontal privacy-preserving federated learning framework for energy load forecasting, namely FedREP. We consider a federated learning system consisting of a control centre and multiple retailers by enabling multiple REPs to build a common, robust machine learning model without sharing data. For forecasting, we use a state-of-the-art Long Short-Term Memory (LSTM) neural network due to its ability to learn long term sequences of observations.
  arXiv  Detail & Related papers  (2022-03-01T04:16:19Z)
• Electricity Consumption Forecasting for Out-of-distribution Time-of-Use Tariffs [14.524613608854242]
  In electricity markets, retailers or brokers want to maximize profits by allocating tariff profiles to end consumers. We consider a greedy solution to maximize the overall profit for brokers by optimal tariff profile allocation. This in-turn requires forecasting electricity consumption for each user for all tariff profiles.
  arXiv  Detail & Related papers  (2022-02-11T09:13:55Z)
• Secure Federated Learning for Residential Short Term Load Forecasting [0.34123736336071864]
  This paper examines a collaborative machine learning method for short-term demand forecasting using smart meter data. The methods evaluated take into account several scenarios that explore how traditional centralized approaches could be projected in the direction of a decentralized, collaborative and private system.
  arXiv  Detail & Related papers  (2021-11-17T17:27:59Z)
• Prediction of Energy Consumption for Variable Customer Portfolios Including Aleatoric Uncertainty Estimation [0.0]
  We propose a method to calculate hourly day-ahead energy consumption forecasts using deep neural networks. To consider the statistical properties of energy consumption values, the aleatoric uncertainty is modeled using lognormal distributions. As a result, predictions of the hourly day-ahead energy consumption of single customers are represented by random variables drawn from lognormal distributions.
  arXiv  Detail & Related papers  (2021-10-01T19:18:13Z)
• DER Forecast using Privacy Preserving Federated Learning [0.0]
  A distributed machine learning approach, Federated Learning, is proposed to carry out DER forecasting using a network of IoT nodes. We consider a simulation study which includes 1000 DERs, and show that our method leads to an accurate prediction of preserve consumer privacy.
  arXiv  Detail & Related papers  (2021-07-07T14:25:43Z)
• Predicting seasonal influenza using supermarket retail records [59.18952050885709]
  We consider supermarket retail data as a proxy signal for influenza, through the identification of sentinel baskets. We make use of the Support Vector Regression (SVR) model to produce the predictions of seasonal flu incidence.
  arXiv  Detail & Related papers  (2020-12-08T16:30:43Z)

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.