Pattern Similarity-based Machine Learning Methods for Mid-term Load Forecasting: A Comparative Study

• URL: http://arxiv.org/abs/2003.01475v1
• Date: Tue, 3 Mar 2020 12:14:36 GMT
• Title: Pattern Similarity-based Machine Learning Methods for Mid-term Load Forecasting: A Comparative Study
• Authors: Grzegorz Dudek, Pawe{\l} Pe{\l}ka
• Abstract summary: We use pattern similarity-based methods for forecasting monthly electricity demand expressing annual seasonality. An integral part of the models is the time series representation using patterns of time series sequences. We consider four such models: nearest neighbor model, fuzzy neighborhood model, kernel regression model and general regression neural network.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pattern similarity-based methods are widely used in classification and regression problems. Repeated, similar-shaped cycles observed in seasonal time series encourage to apply these methods for forecasting. In this paper we use the pattern similarity-based methods for forecasting monthly electricity demand expressing annual seasonality. An integral part of the models is the time series representation using patterns of time series sequences. Pattern representation ensures the input and output data unification through trend filtering and variance equalization. Consequently, pattern representation simplifies the forecasting problem and allows us to use models based on pattern similarity. We consider four such models: nearest neighbor model, fuzzy neighborhood model, kernel regression model and general regression neural network. A regression function is constructed by aggregation output patterns with weights dependent on the similarity between input patterns. The advantages of the proposed models are: clear principle of operation, small number of parameters to adjust, fast optimization procedure, good generalization ability, working on the newest data without retraining, robustness to missing input variables, and generating a vector as an output. In the experimental part of the work the proposed models were used to forecasting the monthly demand for 35 European countries. The model performances were compared with the performances of the classical models such as ARIMA and exponential smoothing as well as state-of-the-art models such as multilayer perceptron, neuro-fuzzy system and long short-term memory model. The results show high performance of the proposed models which outperform the comparative models in accuracy, simplicity and ease of optimization.

Related papers

• Supervised Score-Based Modeling by Gradient Boosting [49.556736252628745]
  We propose a Supervised Score-based Model (SSM) which can be viewed as a gradient boosting algorithm combining score matching. We provide a theoretical analysis of learning and sampling for SSM to balance inference time and prediction accuracy. Our model outperforms existing models in both accuracy and inference time.
  arXiv  Detail & Related papers  (2024-11-02T07:06:53Z)
• Predictive Modeling in the Reservoir Kernel Motif Space [0.9217021281095907]
  This work proposes a time series prediction method based on the kernel view of linear reservoirs. We provide a geometric interpretation of our approach shedding light on how our approach is related to the core reservoir models. Empirical experiments then compare predictive performances of our suggested model with those of recent state-of-art transformer based models.
  arXiv  Detail & Related papers  (2024-05-11T16:12:25Z)
• Random Models for Fuzzy Clustering Similarity Measures [0.0]
  The Adjusted Rand Index (ARI) is a widely used method for comparing hard clusterings. We propose a single framework for computing the ARI with three random models that are intuitive and explainable for both hard and fuzzy clusterings.
  arXiv  Detail & Related papers  (2023-12-16T00:07:04Z)
• On the Generalization and Adaption Performance of Causal Models [99.64022680811281]
  Differentiable causal discovery has proposed to factorize the data generating process into a set of modules. We study the generalization and adaption performance of such modular neural causal models. Our analysis shows that the modular neural causal models outperform other models on both zero and few-shot adaptation in low data regimes.
  arXiv  Detail & Related papers  (2022-06-09T17:12:32Z)
• 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)
• Time Adaptive Gaussian Model [0.913755431537592]
  Our model is a generalization of state-of-the-art methods for the inference of temporal graphical models. It performs pattern recognition by clustering data points in time; and, it finds probabilistic (and possibly causal) relationships among the observed variables.
  arXiv  Detail & Related papers  (2021-02-02T00:28:14Z)
• Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modeling [54.94763543386523]
  Current autoencoder-based disentangled representation learning methods achieve disentanglement by penalizing the ( aggregate) posterior to encourage statistical independence of the latent factors. We present a novel multi-stage modeling approach where the disentangled factors are first learned using a penalty-based disentangled representation learning method. Then, the low-quality reconstruction is improved with another deep generative model that is trained to model the missing correlated latent variables.
  arXiv  Detail & Related papers  (2020-10-25T18:51:15Z)
• Goal-directed Generation of Discrete Structures with Conditional Generative Models [85.51463588099556]
  We introduce a novel approach to directly optimize a reinforcement learning objective, maximizing an expected reward. We test our methodology on two tasks: generating molecules with user-defined properties and identifying short python expressions which evaluate to a given target value.
  arXiv  Detail & Related papers  (2020-10-05T20:03:13Z)
• Control as Hybrid Inference [62.997667081978825]
  We present an implementation of CHI which naturally mediates the balance between iterative and amortised inference. We verify the scalability of our algorithm on a continuous control benchmark, demonstrating that it outperforms strong model-free and model-based baselines.
  arXiv  Detail & Related papers  (2020-07-11T19:44:09Z)
• Semi-nonparametric Latent Class Choice Model with a Flexible Class Membership Component: A Mixture Model Approach [6.509758931804479]
  The proposed model formulates the latent classes using mixture models as an alternative approach to the traditional random utility specification. Results show that mixture models improve the overall performance of latent class choice models.
  arXiv  Detail & Related papers  (2020-07-06T13:19:26Z)
• Ensemble Forecasting of Monthly Electricity Demand using Pattern Similarity-based Methods [0.0]
  This work presents ensemble forecasting of monthly electricity demand using pattern similarity-based forecasting methods (PSFMs) PSFMs applied in this study include $k$-nearest neighbor model, fuzzy neighborhood model, kernel regression model, and general regression neural network. An empirical illustration applies the ensemble models as well as individual PSFMs for comparison to the monthly electricity demand forecasting for 35 European countries.
  arXiv  Detail & Related papers  (2020-03-29T17:26:58Z)

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.