A Hybrid Model for Forecasting Short-Term Electricity Demand

• URL: http://arxiv.org/abs/2205.10449v1
• Date: Fri, 20 May 2022 22:13:25 GMT
• Title: A Hybrid Model for Forecasting Short-Term Electricity Demand
• Authors: Maria Eleni Athanasopoulou, Justina Deveikyte, Alan Mosca, Ilaria Peri and Alessandro Provetti
• Abstract summary: Currently the UK Electric market is guided by load (demand) forecasts published every thirty minutes by the regulator. We present HYENA: a hybrid predictive model that combines feature engineering (selection of the candidate predictor features), mobile-window predictors and LSTM encoder-decoders.
• Score: 59.372588316558826
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Currently the UK Electric market is guided by load (demand) forecasts published every thirty minutes by the regulator. A key factor in predicting demand is weather conditions, with forecasts published every hour. We present HYENA: a hybrid predictive model that combines feature engineering (selection of the candidate predictor features), mobile-window predictors and finally LSTM encoder-decoders to achieve higher accuracy with respect to mainstream models from the literature. HYENA decreased MAPE loss by 16\% and RMSE loss by 10\% over the best available benchmark model, thus establishing a new state of the art for the UK electric load (and price) forecasting.

Related papers

• Short-Term Electricity Demand Forecasting of Dhaka City Using CNN with Stacked BiLSTM [0.471858286267785]
  This paper proposes a hybrid model of Convolutional Neural Network (CNN) and stacked Bidirectional Long-short Term Memory (BiLSTM) architecture to perform an accurate short-term forecast of the electricity demand of Dhaka city. The proposed approach produced the best prediction results in comparison to the other benchmark models used in the study, with MAPE 1.64%, MSE 0.015, RMSE 0.122 and MAE 0.092.
  arXiv  Detail & Related papers  (2024-06-10T09:02:07Z)
• Revisiting Day-ahead Electricity Price: Simple Model Save Millions [7.088576782842557]
  We propose a simple piecewise linear model that significantly enhances forecast accuracy by directly deriving prices from readily forecastable demand-supply values. Experiments in the day-ahead electricity markets of Shanxi province and ISO New England reveal that such forecasts could potentially save residents millions of dollars a year.
  arXiv  Detail & Related papers  (2024-05-20T08:27:14Z)
• ExtremeCast: Boosting Extreme Value Prediction for Global Weather Forecast [57.6987191099507]
  We introduce Exloss, a novel loss function that performs asymmetric optimization and highlights extreme values to obtain accurate extreme weather forecast. We also introduce ExBooster, which captures the uncertainty in prediction outcomes by employing multiple random samples. Our solution can achieve state-of-the-art performance in extreme weather prediction, while maintaining the overall forecast accuracy comparable to the top medium-range forecast models.
  arXiv  Detail & Related papers  (2024-02-02T10:34:13Z)
• A probabilistic forecast methodology for volatile electricity prices in the Australian National Electricity Market [0.36832029288386137]
  The South Australia region of the Australian National Electricity Market displays some of the highest levels of price volatility observed in modern electricity markets. This paper outlines an approach to probabilistic forecasting under these extreme conditions, including spike filtration and several post-processing steps.
  arXiv  Detail & Related papers  (2023-11-13T12:33:33Z)
• Electricity Demand Forecasting with Hybrid Statistical and Machine Learning Algorithms: Case Study of Ukraine [0.0]
  The proposed methodology was constructed using hourly data from Ukraine's electricity consumption ranging from 2013 to 2020. Our hybrid model is very effective at forecasting long-term electricity consumption on an hourly resolution.
  arXiv  Detail & Related papers  (2023-04-11T12:15:50Z)
• Beyond S-curves: Recurrent Neural Networks for Technology Forecasting [60.82125150951035]
  We develop an autencoder approach that employs recent advances in machine learning and time series forecasting. S-curves forecasts largely exhibit a mean average percentage error (MAPE) comparable to a simple ARIMA baseline. Our autoencoder approach improves the MAPE by 13.5% on average over the second-best result.
  arXiv  Detail & Related papers  (2022-11-28T14:16:22Z)
• Predictive Accuracy of a Hybrid Generalized Long Memory Model for Short Term Electricity Price Forecasting [0.0]
  This study investigates the predictive performance of a new hybrid model based on the Generalized long memory autoregressive model (k-factor GARMA) The performance of the proposed model is evaluated using data from Nord Pool Electricity markets.
  arXiv  Detail & Related papers  (2022-04-18T12:21:25Z)
• The impact of online machine-learning methods on long-term investment decisions and generator utilization in electricity markets [69.68068088508505]
  We investigate the impact of eleven offline and five online learning algorithms to predict the electricity demand profile over the next 24h. We show we can reduce the mean absolute error by 30% using an online algorithm when compared to the best offline algorithm. We also show that large errors in prediction accuracy have a disproportionate error on investments made over a 17-year time frame.
  arXiv  Detail & Related papers  (2021-03-07T11:28:54Z)
• Hybrid Modelling Approaches for Forecasting Energy Spot Prices in EPEC market [62.997667081978825]
  We consider several hybrid modelling approaches for forecasting energy spot prices in EPEC market. Data was given in terms of electricity prices for 2013-2014 years, and test data as a year of 2015.
  arXiv  Detail & Related papers  (2020-10-14T12:45:53Z)
• A generative adversarial network approach to (ensemble) weather prediction [91.3755431537592]
  We use a conditional deep convolutional generative adversarial network to predict the geopotential height of the 500 hPa pressure level, the two-meter temperature and the total precipitation for the next 24 hours over Europe. The proposed models are trained on 4 years of ERA5 reanalysis data from 2015-2018 with the goal to predict the associated meteorological fields in 2019.
  arXiv  Detail & Related papers  (2020-06-13T20:53:17Z)

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.