Mid-term Load Pattern Forecasting With Recurrent Artificial Neural Network

Baek, Seung‐Mook

doi:10.1109/access.2019.2957072

Cited by 31 publications

(20 citation statements)

References 23 publications

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“…Moreover, the load prediction results could reduce the electricity costs and help shave the peak load in the district [14]. The ANN model can also have good performance on mid-term daily peak load prediction [15]. Song et al proposes a heating load prediction model based on temporal convolutional neural network (TCN).…”

Section: A Load Prediction Methodsmentioning

confidence: 99%

Ultra-Short-Term Building Cooling Load Prediction Model Based on Feature Set Construction and Ensemble Machine Learning

Ding

Kong

et al. 2020

IEEE Access

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As the requirements for the optimal control of building systems increase, the accuracy and speed of load predictions should also increase. However, the accuracy of load predictions is related to not only the prediction algorithm, but also the feature set construction. Therefore, this study develops a shortterm building cooling load prediction model based on feature set construction. The impacts of four different feature set construction methods-feature extraction, correlation analysis, K-means clustering, and discrete wavelet transform (DWT)-on the prediction accuracy are compared. To ensure that the effect of the feature set construction method is universal, three different prediction algorithms are used. The influences of the sample dimension and prediction time horizon on the prediction accuracy are also analysed. The prediction model is developed based on an ensemble learning algorithm utilising the cubist algorithm, and the performance of the prediction model is improved when DWT is used for constructing the feature set. Compared with other commonly used prediction models, the proposed model exhibits the best performance, with R-squared and CV-RMSE values of 99.8% and 1.5%, respectively. INDEX TERMS Cooling load prediction, Feature extraction, Ensemble learning algorithms, Discrete wavelet transform Nomenclature RF Random forest R-squared Square correlation coefficient GBM Gradient boosting machine FOG First order 5-h gradient DWT Discrete wavelet transform MSG Mean value of the square of the 1-h gradients in the past 5 h CA Correlation analysis FS Feature sets PCA Principal component analysis Large sample FS1 t-SNE t-distributed stochastic neighbour embedding Small sample FS10 g Principal component contribution ratio ARE Absolute value of relative error between predicted and actual values rspearman Spearman rank correlation coefficient IR Degree of improvement in the prediction accuracy Dk Sum of intra-cluster distances At Actual value SSE Sum of the squared error Ft Predicted value MSE Mean square error n Sample size CV-RMSE Root-mean-square error coefficient of variation Reduction ratio Reduction values between two time points

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Section: A Load Prediction Methodsmentioning

confidence: 99%

Ultra-Short-Term Building Cooling Load Prediction Model Based on Feature Set Construction and Ensemble Machine Learning

Ding

Kong

et al. 2020

IEEE Access

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“…ANN is popular choice for load forecasting technique. A recurrent type ANN, proposed by Baek [118], have shown an MAPE value of 1.57% for MTLF in South Korea. In this study, dataset includes daily load consumption, real-time temperature, weather, and day type from July, 2011, a week before 168h load data as the training set and July 8th, 24 hour of the same year as test set.…”

Section: B Artificial Neural Network (Ann)mentioning

confidence: 99%

A Comprehensive Review of the Load Forecasting Techniques Using Single and Hybrid Predictive Models

et al. 2020

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Load forecasting is a pivotal part of the power utility companies. To provide load-shedding free and uninterrupted power to the consumer, decision-makers in the utility sector must forecast the future demand for electricity with a minimum amount of error percentage. Load prediction with less percentage of error can save millions of dollars to the utility companies. There are numerous Machine Learning (ML) techniques to amicably forecast the demand of electricity among which the hybrid models show the best result. Two or more than two predictive models are amalgamated to design a hybrid model, each of which provides improved performances by the merit of individual algorithms. This paper reviews the current stateof-the-art of electric load forecasting technologies and presents recent works pertaining to the combination of different ML algorithms into two or more methods for the construction of hybrid models. A comprehensive study of each single and multiple load forecasting model is performed with an in-depth analysis of their advantages, disadvantages, and functions. A comparison between their performance in terms of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) values are developed with pertinent literature of several models to aid the researchers with the selection of suitable models for load prediction.

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“…Of these, neural networks (NNs) are the most explored because of their attractive features such as learning capability, universal approximation property, nonlinear modeling, massive parallelism, and ease of specifying a loss function, to align it better with forecasting goals. Some examples of using different architectures of NNs for MLTF are: [6] where NN learns on historical demand and weather factors, [7] where Kohonen NN was used, [8] where NNs were supported by fuzzy logic, [9] where generalized regression NN was used, [10] where NNs, linear regression, and AdaBoost were used, [11] where weighted evolving fuzzy NNs were combined, and [12] where recurrent NNs were used. Among other ML MTLF models, the following can be mentioned: support vector machines [13] and pattern similaritybased models [14].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Residual Dilated LSTM and Exponential Smoothing Model for Midterm Electric Load Forecasting

Dudek¹,

Pełka²,

Smyl

2022

IEEE Trans. Neural Netw. Learning Syst.

118

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This work presents a hybrid and hierarchical deep learning model for midterm load forecasting. The model combines exponential smoothing (ETS), advanced long short-term memory (LSTM), and ensembling. ETS extracts dynamically the main components of each individual time series and enables the model to learn their representation. Multilayer LSTM is equipped with dilated recurrent skip connections and a spatial shortcut path from lower layers to allow the model to better capture longterm seasonal relationships and ensure more efficient training. A common learning procedure for LSTM and ETS, with a penalized pinball loss, leads to simultaneous optimization of data representation and forecasting performance. In addition, ensembling at three levels ensures a powerful regularization. A simulation study performed on the monthly electricity demand time series for 35 European countries confirmed the high performance of the proposed model and its competitiveness with classical models such as ARIMA and ETS as well as state-ofthe-art models based on machine learning.

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Mid-term Load Pattern Forecasting With Recurrent Artificial Neural Network

Cited by 31 publications

References 23 publications

Ultra-Short-Term Building Cooling Load Prediction Model Based on Feature Set Construction and Ensemble Machine Learning

Ultra-Short-Term Building Cooling Load Prediction Model Based on Feature Set Construction and Ensemble Machine Learning

A Comprehensive Review of the Load Forecasting Techniques Using Single and Hybrid Predictive Models

A Hybrid Residual Dilated LSTM and Exponential Smoothing Model for Midterm Electric Load Forecasting

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