Application of SOM neural networks to short-term load forecasting: The Spanish electricity market case study

López, María Dolores Ruiz; Verdú, S.; Senabre, C.; Aparicio, Juan; Gabaldón, A.

doi:10.1016/j.epsr.2012.04.009

Cited by 91 publications

(32 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The errors reported for the year 2006 were around a 1.76% and 2.88% for Australian and New York markets, respectively. A SOM without combining with another technique was presented in [64] to predict the prices for the Spanish electricity market. A preprocessing to select the input variables was proposed as a previous step to the prediction, which was obtained from the prices of the nearest centroid to the input data.…”

Section: Related Workmentioning

confidence: 99%

A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting

et al. 2015

View full text Add to dashboard Cite

Data mining has become an essential tool during the last decade to analyze large sets of data. The variety of techniques it includes and the successful results obtained in many application fields, make this family of approaches powerful and widely used. In particular, this work explores the application of these techniques to time series forecasting. Although classical statistical-based methods provides reasonably good results, the result of the application of data mining outperforms those of classical ones. Hence, this work faces two main challenges: (i) to provide a compact mathematical formulation of the mainly used techniques; (ii) to review the latest works of time series forecasting and, as case study, those related to electricity price and demand markets.

show abstract

Section: Related Workmentioning

confidence: 99%

A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting

et al. 2015

View full text Add to dashboard Cite

show abstract

“…This fact makes it difficult to model time series using a liner model, therefore a nonlinear approach should be suggested. A nonlinear autoregressive neural network [26,27], applied to time series forecasting, describes a discrete, non-linear, autoregressive model that can be written as follows [28]:…”

Section: Nar Modelmentioning

confidence: 99%

An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings

et al. 2016

View full text Add to dashboard Cite

This paper addresses the problem of energy consumption prediction using neural networks over a set of public buildings. Since energy consumption in the public sector comprises a substantial share of overall consumption, the prediction of such consumption represents a decisive issue in the achievement of energy savings. In our experiments, we use the data provided by an energy consumption monitoring system in a compound of faculties and research centers at the University of Granada, and provide a methodology to predict future energy consumption using nonlinear autoregressive (NAR) and the nonlinear autoregressive neural network with exogenous inputs (NARX), respectively. Results reveal that NAR and NARX neural networks are both suitable for performing energy consumption prediction, but also that exogenous data may help to improve the accuracy of predictions.

show abstract

“…The existing load profiling based load forecasting literature can be classified into two broad categories: a) Studies using only the clustering algorithm and b) studies combining the clustering with a common forecaster [10]- [14]. The proposed model belongs to the second category.…”

Section: Introductionmentioning

confidence: 98%

Hybrid computational intelligence model for Short-Term bus load forecasting

Panapakidis

Christoforidis

Papagiannis

2015

2015 IEEE 15th International Conference on Environment and Electrical Engineering (EEEIC)

View full text Add to dashboard Cite

Distribution Generation (DG) technologies correspond to a technical field of increased interest since their application aid on system security and reliability. In order to bring forth the fully potential of DG, a robust forecasting tool is especially designed for small sized loads at the various buses. Bus load exhibit low correlation compared to the total system`s load; the presence of outlier loads is more regular and the load pattern presents high degree of stochasticity. Thus a load forecasting model designed for the system`s load is likely to show poor performance. This work proposes a hybrid bus load forecasting tool. The hybridization refers to the combined use of a clustering process with a feed-forward Artificial Neural Network (ANN). The proposed model is tested at four buses within the Greek interconnected system and simulation results highlight the efficiency of the model.

show abstract

Application of SOM neural networks to short-term load forecasting: The Spanish electricity market case study

Cited by 91 publications

References 17 publications

A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting

A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting

An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings

Hybrid computational intelligence model for Short-Term bus load forecasting

Contact Info

Product

Resources

About