Analysis of Weather and Time Features in Machine Learning-aided ERCOT Load Forecasting

• URL: http://arxiv.org/abs/2310.08793v1
• Date: Fri, 13 Oct 2023 00:46:12 GMT
• Title: Analysis of Weather and Time Features in Machine Learning-aided ERCOT Load Forecasting
• Authors: Jonathan Yang, Mingjian Tuo, Jin Lu, Xingpeng Li
• Abstract summary: This work develops several machine learning (ML) models that take various time and weather information as part of the input features to predict the short-term system-wide total load. Actual load and historical weather data for the same region were processed and then used to train the ML models. Case studies demonstrated the effectiveness of ML models trained with different weather and time input features for ERCOT load forecasting.
• Score: 0.2184775414778289
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate load forecasting is critical for efficient and reliable operations of the electric power system. A large part of electricity consumption is affected by weather conditions, making weather information an important determinant of electricity usage. Personal appliances and industry equipment also contribute significantly to electricity demand with temporal patterns, making time a useful factor to consider in load forecasting. This work develops several machine learning (ML) models that take various time and weather information as part of the input features to predict the short-term system-wide total load. Ablation studies were also performed to investigate and compare the impacts of different weather factors on the prediction accuracy. Actual load and historical weather data for the same region were processed and then used to train the ML models. It is interesting to observe that using all available features, each of which may be correlated to the load, is unlikely to achieve the best forecasting performance; features with redundancy may even decrease the inference capabilities of ML models. This indicates the importance of feature selection for ML models. Overall, case studies demonstrated the effectiveness of ML models trained with different weather and time input features for ERCOT load forecasting.

Related papers

• Gridded Transformer Neural Processes for Large Unstructured Spatio-Temporal Data [47.14384085714576]
  We introduce gridded pseudo-tokenPs to handle unstructured observations and a processor containing gridded pseudo-tokens that leverage efficient attention mechanisms. Our method consistently outperforms a range of strong baselines on various synthetic and real-world regression tasks involving large-scale data. The real-life experiments are performed on weather data, demonstrating the potential of our approach to bring performance and computational benefits when applied at scale in a weather modelling pipeline.
  arXiv  Detail & Related papers  (2024-10-09T10:00:56Z)
• Efficient mid-term forecasting of hourly electricity load using generalized additive models [0.0]
  We propose a novel forecasting method using Generalized Additive Models (GAMs) built from interpretable P-splines and enhanced with autoregressive post-processing. The proposed model is evaluated on load data from 24 European countries.
  arXiv  Detail & Related papers  (2024-05-27T11:41:41Z)
• Generalizing Weather Forecast to Fine-grained Temporal Scales via Physics-AI Hybrid Modeling [55.13352174687475]
  This paper proposes a physics-AI hybrid model (i.e., WeatherGFT) which Generalizes weather forecasts to Finer-grained Temporal scales. Specifically, we employ a carefully designed PDE kernel to simulate physical evolution on a small time scale. We introduce a lead time-aware training framework to promote the generalization of the model at different lead times.
  arXiv  Detail & Related papers  (2024-05-22T16:21:02Z)
• 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)
• 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)
• Interactive Generalized Additive Model and Its Applications in Electric Load Forecasting [12.431475555894089]
  In this paper, we propose an interactive GAM which is not only interpretable but also can incorporate specific domain knowledge in electric power industry. Our interactive GAM outperforms current state-of-the-art methods and demonstrates good generalization ability in the cases of extreme weather events. We launched a user-friendly web-based tool based on interactive GAM and already incorporated it into our eForecaster product.
  arXiv  Detail & Related papers  (2023-10-24T09:17:47Z)
• Benchmarks and Custom Package for Energy Forecasting [55.460452605056894]
  Energy forecasting aims to minimize the cost of subsequent tasks such as power grid dispatch. In this paper, we collected large-scale load datasets and released a new renewable energy dataset. We conducted extensive experiments with 21 forecasting methods in these energy datasets at different levels under 11 evaluation metrics.
  arXiv  Detail & Related papers  (2023-07-14T06:50:02Z)
• A comparative assessment of deep learning models for day-ahead load forecasting: Investigating key accuracy drivers [2.572906392867547]
  Short-term load forecasting (STLF) is vital for the effective and economic operation of power grids and energy markets. Several deep learning models have been proposed in the literature for STLF, reporting promising results.
  arXiv  Detail & Related papers  (2023-02-23T17:11:04Z)
• GraphCast: Learning skillful medium-range global weather forecasting [107.40054095223779]
  We introduce a machine learning-based method called "GraphCast", which can be trained directly from reanalysis data. It predicts hundreds of weather variables, over 10 days at 0.25 degree resolution globally, in under one minute. We show that GraphCast significantly outperforms the most accurate operational deterministic systems on 90% of 1380 verification targets.
  arXiv  Detail & Related papers  (2022-12-24T18:15:39Z)
• Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection [60.89616629421904]
  Lidar-based object detectors are critical parts of the 3D perception pipeline in autonomous navigation systems such as self-driving cars. They are sensitive to adverse weather conditions such as rain, snow and fog due to reduced signal-to-noise ratio (SNR) and signal-to-background ratio (SBR)
  arXiv  Detail & Related papers  (2021-07-14T21:10:47Z)
• Smart Weather Forecasting Using Machine Learning:A Case Study in Tennessee [2.9477900773805032]
  We present a weather prediction technique that utilizes historical data from multiple weather stations to train simple machine learning models. The accuracy of the models is good enough to be used alongside the current state-of-the-art techniques.
  arXiv  Detail & Related papers  (2020-08-25T02:41:32Z)

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.