Deep Neural Network Based Demand Side Short Term Load Forecasting

Ryu, Seunghyoung; Noh, Jaekoo; Kim, Hongseok

doi:10.3390/en10010003

Cited by 306 publications

(182 citation statements)

References 28 publications

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“…Meanwhile, Kuremoto successfully used Kennedy's particle swarm optimization in selecting their model parameters [18]. The work most similar to ours is Ryu et al, who found that two different types of examined DNNs performed better on short-term load forecasting of electricity than shallow neural networks and a double seasonal Holt-Winters model [19].…”

Section: Prior Worksupporting

confidence: 60%

Short-Term Load Forecasting of Natural Gas with Deep Neural Network Regression †

2018

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Deep neural networks are proposed for short-term natural gas load forecasting. Deep learning has proven to be a powerful tool for many classification problems seeing significant use in machine learning fields such as image recognition and speech processing. We provide an overview of natural gas forecasting. Next, the deep learning method, contrastive divergence is explained. We compare our proposed deep neural network method to a linear regression model and a traditional artificial neural network on 62 operating areas, each of which has at least 10 years of data. The proposed deep network outperforms traditional artificial neural networks by 9.83% weighted mean absolute percent error (WMAPE).

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Section: Prior Worksupporting

confidence: 60%

Short-Term Load Forecasting of Natural Gas with Deep Neural Network Regression †

2018

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“…The multiple computation layers structure increases the feature abstraction capability of the network, which makes them more efficient in learning complex non-linear patterns [32]. To the best of our knowledge and according to Ryu et al [14] in 2017, only a few DNN-based load forecasting models are proposed. Recently, in 2016, Mocanu et al [1] employed, a deep learning approach based on a restricted Boltzmann machine, for single-meter residential load forecasting.…”

Section: Introductionmentioning

confidence: 99%

“…The same year (2016), Marino et al [33] presented a novel energy load forecasting methodology using two different deep architectures namely, a standard LSTM and an LSTM with sequence to Sequence architecture that produced promising results. In 2017, Ryu et al [14] proposed a DNN based framework for day-ahead load forecast by training DNNs in two different ways, using a pre-training restricted Boltzmann machine and using the rectified linear unit without pre-training. This model exhibited accurate and robust predictions compared to shallow neural network and others alternatives (i.e., double seasonal Holt-Winters model and the autoregressive integrated moving average model).…”

Section: Introductionmentioning

confidence: 99%

Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †

et al. 2018

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Abstract:Background: With the development of smart grids, accurate electric load forecasting has become increasingly important as it can help power companies in better load scheduling and reduce excessive electricity production. However, developing and selecting accurate time series models is a challenging task as this requires training several different models for selecting the best amongst them along with substantial feature engineering to derive informative features and finding optimal time lags, a commonly used input features for time series models. Methods: Our approach uses machine learning and a long short-term memory (LSTM)-based neural network with various configurations to construct forecasting models for short to medium term aggregate load forecasting. The research solves above mentioned problems by training several linear and non-linear machine learning algorithms and picking the best as baseline, choosing best features using wrapper and embedded feature selection methods and finally using genetic algorithm (GA) to find optimal time lags and number of layers for LSTM model predictive performance optimization. Results: Using France metropolitan's electricity consumption data as a case study, obtained results show that LSTM based model has shown high accuracy then machine learning model that is optimized with hyperparameter tuning. Using the best features, optimal lags, layers and training various LSTM configurations further improved forecasting accuracy. Conclusions: A LSTM model using only optimally selected time lagged features captured all the characteristics of complex time series and showed decreased Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) for medium to long range forecasting for a wider metropolitan area.

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“…Even though our framework is independent of short-term load forecasting techniques, we use the double seasonal Holt-Winters (DSHW) [15], which is one of the popular time series load forecasting techniques. Note that, however, our method is compliant with any kind of load forecasting methods such as the newly developed deep neural network (DNN)-based model [16,17]. Then, we compute the error distribution between predicted and real load profiles, which is used to set robust margin, or called robust proportion hereafter.…”

Section: Robust Ess Operation Frameworkmentioning

confidence: 99%

Robust Operation of Energy Storage System with Uncertain Load Profiles

et al. 2017

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Abstract:In this paper, we propose novel techniques to reduce total cost and peak load of factories from a customer point of view. We control energy storage system (ESS) to minimize the total electricity bill under the Korea commercial and industrial (KCI) tariff, which both considers peak load and time of use (ToU). Under the KCI tariff, the average peak load, which is the maximum among all average power consumptions measured every 15 min for the past 12 months, determines the monthly base cost, and thus peak load control is extremely critical. We aim to leverage ESS for both peak load reduction based on load prediction as well as energy arbitrage exploiting ToU. However, load prediction inevitably has uncertainty, which makes ESS operation challenging with KCI tariff. To tackle it, we apply robust optimization to minimize risk in a real environment. Our approach significantly reduces the peak load by 49.9% and the total cost by 10.8% compared to the case that does not consider load uncertainty. In doing this we also consider battery degradation cost and validate the practical use of the proposed techniques.

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Deep Neural Network Based Demand Side Short Term Load Forecasting

Cited by 306 publications

References 28 publications

Short-Term Load Forecasting of Natural Gas with Deep Neural Network Regression †

Short-Term Load Forecasting of Natural Gas with Deep Neural Network Regression †

Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †

Robust Operation of Energy Storage System with Uncertain Load Profiles

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