Short-term Prediction of Household Electricity Consumption Using Customized LSTM and GRU Models

• URL: http://arxiv.org/abs/2212.08757v1
• Date: Fri, 16 Dec 2022 23:42:57 GMT
• Title: Short-term Prediction of Household Electricity Consumption Using Customized LSTM and GRU Models
• Authors: Saad Emshagin, Wayes Koroni Halim, Rasha Kashef
• Abstract summary: This paper proposes a customized GRU (Gated Recurrent Unit) and Long Short-Term Memory (LSTM) architecture to address this challenging problem. The electricity consumption datasets were obtained from individual household smart meters.
• Score: 5.8010446129208155
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: With the evolution of power systems as it is becoming more intelligent and interactive system while increasing in flexibility with a larger penetration of renewable energy sources, demand prediction on a short-term resolution will inevitably become more and more crucial in designing and managing the future grid, especially when it comes to an individual household level. Projecting the demand for electricity for a single energy user, as opposed to the aggregated power consumption of residential load on a wide scale, is difficult because of a considerable number of volatile and uncertain factors. This paper proposes a customized GRU (Gated Recurrent Unit) and Long Short-Term Memory (LSTM) architecture to address this challenging problem. LSTM and GRU are comparatively newer and among the most well-adopted deep learning approaches. The electricity consumption datasets were obtained from individual household smart meters. The comparison shows that the LSTM model performs better for home-level forecasting than alternative prediction techniques-GRU in this case. To compare the NN-based models with contrast to the conventional statistical technique-based model, ARIMA based model was also developed and benchmarked with LSTM and GRU model outcomes in this study to show the performance of the proposed model on the collected time series data.

Related papers

• AI-Powered Predictions for Electricity Load in Prosumer Communities [0.0]
  We present and test artificial intelligence powered short-term load forecasting methodologies. Results show that the combination of persistent and regression terms (adapted to the load forecasting task) achieves the best forecast accuracy.
  arXiv  Detail & Related papers  (2024-02-21T12:23:09Z)
• Grid Frequency Forecasting in University Campuses using Convolutional LSTM [0.0]
  This paper harnesses Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to establish robust time forecasting models for grid frequency. Individual ConvLSTM models are trained on power consumption data for each campus building and forecast the grid frequency based on historical trends. An Ensemble Model is formulated to aggregate insights from the building-specific models, delivering comprehensive forecasts for the entire campus.
  arXiv  Detail & Related papers  (2023-10-24T13:53:51Z)
• Transfer learning for day-ahead load forecasting: a case study on European national electricity demand time series [42.156081667752886]
  Short-term load forecasting (STLF) is crucial for the daily operation of power grids. Various forecasting approaches have been proposed for improving STLF, including neural network (NN) models. We employ a popular and easy-to-implement NN model and perform a clustering analysis to identify similar patterns among the series.
  arXiv  Detail & Related papers  (2023-10-24T06:54:50Z)
• Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution [57.71199089609161]
  Long-term time-series forecasting (LTTF) has become a pressing demand in many applications, such as wind power supply planning. Transformer models have been adopted to deliver high prediction capacity because of the high computational self-attention mechanism. We propose an efficient Transformerbased model, named Conformer, which differentiates itself from existing methods for LTTF in three aspects.
  arXiv  Detail & Related papers  (2023-01-05T13:59:29Z)
• Cascaded Deep Hybrid Models for Multistep Household Energy Consumption Forecasting [5.478764356647437]
  This study introduces two hybrid cascaded models for forecasting multistep household power consumption in different resolutions. The proposed hybrid models achieve superior prediction performance compared to the existing multistep power consumption prediction methods.
  arXiv  Detail & Related papers  (2022-07-06T11:02:23Z)
• Your Autoregressive Generative Model Can be Better If You Treat It as an Energy-Based One [83.5162421521224]
  We propose a unique method termed E-ARM for training autoregressive generative models. E-ARM takes advantage of a well-designed energy-based learning objective. We show that E-ARM can be trained efficiently and is capable of alleviating the exposure bias problem.
  arXiv  Detail & Related papers  (2022-06-26T10:58:41Z)
• Advanced Statistical Learning on Short Term Load Process Forecasting [13.466565318976887]
  Short Term Load Forecast (STLF) is necessary for effective scheduling, operation optimization trading, and decision-making for electricity consumers. We propose different statistical nonlinear models to manage these challenges of hard type datasets and forecast 15-min frequency electricity load up to 2-days ahead.
  arXiv  Detail & Related papers  (2021-10-19T12:32:40Z)
• Demand Forecasting in Smart Grid Using Long Short-Term Memory [0.0]
  Long Short-Term Memory (LSTM) shows promising results in predicting time series data. In this paper, an LSTM based model using neural network architecture is proposed to forecast power demand.
  arXiv  Detail & Related papers  (2021-07-28T21:45:54Z)
• Learning representations with end-to-end models for improved remaining useful life prognostics [64.80885001058572]
  The remaining Useful Life (RUL) of equipment is defined as the duration between the current time and its failure. We propose an end-to-end deep learning model based on multi-layer perceptron and long short-term memory layers (LSTM) to predict the RUL. We will discuss how the proposed end-to-end model is able to achieve such good results and compare it to other deep learning and state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-04-11T16:45:18Z)
• Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series [43.86737761236125]
  We propose a novel generative model, which tracks the transition of latent clusters, instead of isolated feature representations. It is characterized by a newly designed dynamic Gaussian mixture distribution, which captures the dynamics of clustering structures. A structured inference network is also designed for enabling inductive analysis.
  arXiv  Detail & Related papers  (2021-03-03T04:10:07Z)
• Investigating Underlying Drivers of Variability in Residential Energy Usage Patterns with Daily Load Shape Clustering of Smart Meter Data [53.51471969978107]
  Large-scale deployment of smart meters has motivated increasing studies to explore disaggregated daily load patterns. This paper aims to shed light on the mechanisms by which electricity consumption patterns exhibit variability.
  arXiv  Detail & Related papers  (2021-02-16T16:56:27Z)

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.