Energy cost forecasting for event venues

Zagar, Andrea; Grolinger, Katarina; Capretz, Miriam A. M.; Seewald, Luke

doi:10.1109/epec.2015.7379953

Cited by 3 publications

(3 citation statements)

References 20 publications

(19 reference statements)

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“…The same datasets were used to test their accuracy and percentage errors in terms of R-squared, RMSE, and MAPE. The compared forecasting methods were long short-term memory (LSTM) [46], support vector machine (SVM) [51], [52], k-nearest neighbor (KNN) [53], Bayesian ridge regression (BR) [54], decision tree method (DT) [55], multilayer perceptron (MLP) [56], [57] and ARIMA [6], [7]. To ensure fair comparisons, the datasets were prepared similar to those used in the proposed method, in which they were scaled within 0 and 1 using the MinMaxScaler in Sklearn kit.…”

Section: Comparison Of Proposed Methods With Other Methodsmentioning

confidence: 99%

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Locational Marginal Price Forecasting Using Deep Learning Network Optimized by Mapping-Based Genetic Algorithm

2020

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The convolutional neural network (CNN) is commonly used in visual recognitions and classifications. However, CNN can also be applied as a forecaster that can extract features from spatiotemporal data. This paper proposes a 24h ahead electricity price forecasting method, which integrates CNN with an evolutionary algorithm and utilizes spatiotemporal data. The optimal structure of the CNN network for the locational marginal price (LMP) forecasting was obtained using a genetic algorithm (GA). A gene mapping scheme was initially encoded to represent the search space and the process of selection, mutation, and crossover eliminated structures that did not satisfy the validation fitness function and then competitive individuals were generated. The evolution process uses the root mean square error (RMSE) as the validation fitness function, which is optimzed by training the created CNN network. The proposed gene mapping scheme can be used to design an optimal CNN structure once the mapping between gene binary bits and parameters/hyperparameters of CNN is given. Day-ahead LMP and demand datasets from Pennsylvania-New Jersey-Maryland (PJM) power market were used to demonstrate the evolutionary capability of the proposed method and the finding of optimal CNN structures. Each studied dataset was grouped into 4 subsets corresponding to various seasonal characteristics (different types of situations in real life). Experimental results revealed that the proposed GA-CNN always yielded a higher forecasting accuracy and lower error rates than other forecasting methods. INDEX TERMS Convolutional neural network, deep learning, electricity price forecasting, genetic algorithm, locational marginal price.

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Section: Comparison Of Proposed Methods With Other Methodsmentioning

confidence: 99%

“…For the KNN method, only the values of ''k'' (the number of neighbors) need to be varied. The value of k chosen for testing is from 1 to 25 [53]. The grid search was used to examine the lowest MAPE in determining the best model.…”

Section: Comparison Of Proposed Methods With Other Methodsmentioning

confidence: 99%

Locational Marginal Price Forecasting Using Deep Learning Network Optimized by Mapping-Based Genetic Algorithm

2020

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“…The idea to use the Pareto fronts as a tool to select data in the forecasting process originated from the fact that similar, well-known and described in many articles [25][26][27] machine learning algorithm, k nearest neighbors (kNN), has been successfully applied to that task. This algorithm has been used and described in the literature both as a classification algorithm [28,29] and as a forecasting model.…”

Section: The Idea Behind the Pareto Fronts Usagementioning

confidence: 99%

Usage of the Pareto Fronts as a Tool to Select Data in the Forecasting Process—A Short-Term Electric Energy Demand Forecasting Case

Sabat

Baczyński

2021

Energies

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Transmission, distribution, and micro-grid system operators are struggling with the increasing number of renewables and the changing nature of energy demand. This necessitates the use of prognostic methods based on ever shorter time series. This study depicted an attempt to develop an appropriate method by introducing a novel forecasting model based on the idea to use the Pareto fronts as a tool to select data in the forecasting process. The proposed model was implemented to forecast short-term electric energy demand in Poland using historical hourly demand values from Polish TSO. The study rather intended on implementing the range of different approaches—scenarios of Pareto fronts usage than on a complex evaluation of the obtained results. However, performance of proposed models was compared with a few benchmark forecasting models, including naïve approach, SARIMAX, kNN, and regression. For two scenarios, it has outperformed all other models by minimum 7.7%.

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Evaluation of Smart Infrastructure Systems and Novel UV-Oriented Solution for Integration, Resilience, Inclusiveness, and Sustainability

Zhang¹,

Ren²,

Yuan³

et al. 2020

2020 5th International Conference on Universal Village (UV)

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Energy cost forecasting for event venues

Cited by 3 publications

References 20 publications

Locational Marginal Price Forecasting Using Deep Learning Network Optimized by Mapping-Based Genetic Algorithm

Locational Marginal Price Forecasting Using Deep Learning Network Optimized by Mapping-Based Genetic Algorithm

Usage of the Pareto Fronts as a Tool to Select Data in the Forecasting Process—A Short-Term Electric Energy Demand Forecasting Case

Evaluation of Smart Infrastructure Systems and Novel UV-Oriented Solution for Integration, Resilience, Inclusiveness, and Sustainability

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