scholarly journals Power Demand Forecasting using Long Short-Term Memory (LSTM) Deep-Learning Model for Monitoring Energy Sustainability

2020 ◽  
Vol 12 (3) ◽  
pp. 1109 ◽  
Author(s):  
Choi ◽  
Cho ◽  
Kim
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Demand Management ◽  
Demand Forecasting ◽  
Sudden Change ◽  
Residential Building ◽  
Error Rates ◽  
Seasonal Forecasting ◽  
Power Demand ◽  
Short Term

The purpose of this study is to design a novel custom power demand forecasting algorithm based on the LSTM Deep-Learning method regarding the recent power demand patterns. We performed tests to verify the error rates of the forecasting module, and to confirm the sudden change of power patterns in the actual power demand monitoring system. We collected the power usage data in every five-minute resolution in a day from some groups of the residential, public offices, hospitals, and industrial factories buildings in one year. In order to grasp the external factors and to predict the power demand of each facility, a comparative experiment was conducted in three ways; short-term, long-term, seasonal forecasting exp[eriments. The seasonal patterns of power demand usages were analyzed regarding the residential building. The overall error rates of power demand forecasting using the proposed LSTM module were reduced in terms of each facility. The predicted power demand data shows a certain pattern according to each facility. Especially, the forecasting difference of the residential seasonal forecasting pattern in summer and winter was very different from other seasons. It is possible to reduce unnecessary demand management costs by the designed accurate forecasting method.

scholarly journals Research on power demand forecasting based on attention mechanism and deep learning network

2022 ◽  
Vol 355 ◽  
pp. 02022
Author(s):  
Chenglong Zhang ◽  
Li Yao ◽  
Jinjin Zhang ◽  
Junyong Wu ◽  
Baoguo Shan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Demand Forecasting ◽  
Attention Mechanism ◽  
Power Demand ◽  
Short Term ◽  
Time Step ◽  
Long Short Term Memory ◽  
Deep Learning Network ◽  
Deep Learning Model

Combining actual conditions, power demand forecasting is affected by various uncertain factors such as meteorological factors, economic factors, and diversity of forecasting models, which increase the complexity of forecasting. In response to this problem, taking into account that different time step states will have different effects on the output, the attention mechanism is introduced into the method proposed in this paper, which improves the deep learning model. Improved models of convolutional neural networks (CNN) and long short-term memory (LSTM) that combine the attention mechanism are proposed respectively. Finally, according to the verification results of actual examples, it is proved that the proposed method can obtain a smaller error and the prediction performance are better compared with other models.

scholarly journals Demand Management of Station-Based Car Sharing System Based on Deep Learning Forecasting

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Daben Yu ◽  
Zongping Li ◽  
Qinglun Zhong ◽  
Yi Ai ◽  
Wei Chen
Keyword(s):  
Deep Learning ◽  
Information Technologies ◽  
Short Term Memory ◽  
Demand Management ◽  
Short Term ◽  
Car Sharing ◽  
Real World Data ◽  
Temporal Perspective ◽  
Analytical System ◽  
Operation Status

Metropolitan development has motivated car sharing into an attractive type of car leasing with the help of information technologies. In this paper, we propose a new approach based on deep learning techniques to assess the operation of a station-based car sharing system. First, we analyse the pick-up and drop-off operations of the station-based car sharing system, capturing the operational features of car sharing service and the behaviours of vehicle use from a temporal perspective. Then, we introduced an analytical system to detect the system operation concerning the spontaneous deviations derived from user demands from service provisions. We employed Long Short-Term Memory (LSTM) structure to forecast short-term future vehicle uses. An experimental case based on real-world data is reported to demonstrate the effectiveness of this approach. The results prove that the proposed structure generates high-quality predictions and the operation status derived from user demands.

scholarly journals Short-Term Load Forecasting Using Encoder-Decoder WaveNet: Application to the French Grid

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2524
Author(s):  
Fernando Dorado Rueda ◽  
Jaime Durán Suárez ◽  
Alejandro del Real Torres
Keyword(s):  
Deep Learning ◽  
Time Series Data ◽  
Short Term Memory ◽  
Moving Average ◽  
Demand Forecasting ◽  
Input Sequence ◽  
Superior Performance ◽  
Series Data ◽  
Short Term ◽  
State Of Art

The prediction of time series data applied to the energy sector (prediction of renewable energy production, forecasting prosumers’ consumption/generation, forecast of country-level consumption, etc.) has numerous useful applications. Nevertheless, the complexity and non-linear behaviour associated with such kind of energy systems hinder the development of accurate algorithms. In such a context, this paper investigates the use of a state-of-art deep learning architecture in order to perform precise load demand forecasting 24-h-ahead in the whole country of France using RTE data. To this end, the authors propose an encoder-decoder architecture inspired by WaveNet, a deep generative model initially designed by Google DeepMind for raw audio waveforms. WaveNet uses dilated causal convolutions and skip-connection to utilise long-term information. This kind of novel ML architecture presents different advantages regarding other statistical algorithms. On the one hand, the proposed deep learning model’s training process can be parallelized in GPUs, which is an advantage in terms of training times compared to recurrent networks. On the other hand, the model prevents degradations problems (explosions and vanishing gradients) due to the residual connections. In addition, this model can learn from an input sequence to produce a forecast sequence in a one-shot manner. For comparison purposes, a comparative analysis between the most performing state-of-art deep learning models and traditional statistical approaches is presented: Autoregressive-Integrated Moving Average (ARIMA), Long-Short-Term-Memory, Gated-Recurrent-Unit (GRU), Multi-Layer Perceptron (MLP), causal 1D-Convolutional Neural Networks (1D-CNN) and ConvLSTM (Encoder-Decoder). The values of the evaluation indicators reveal that WaveNet exhibits superior performance in both forecasting accuracy and robustness.

Assessment of Electric Consumption Forecast Using Machine Learning and Deep Learning Models for the Industrial Sector

2022 ◽  
pp. 206-218
Author(s):  
Bhawna Dhupia ◽  
M. Usha Rani
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Short Term Memory ◽  
Rapid Development ◽  
Demand Forecasting ◽  
Industrial Sector ◽  
Power Demand ◽  
Learning Models ◽  
Energy Forecasting ◽  
Evaluation Metric

Power demand forecasting is one of the fields which is gaining popularity for researchers. Although machine learning models are being used for prediction in various fields, they need to upgrade to increase accuracy and stability. With the rapid development of AI technology, deep learning (DL) is being recommended by many authors in their studies. The core objective of the chapter is to employ the smart meter's data for energy forecasting in the industrial sector. In this chapter, the author will be implementing popular power demand forecasting models from machine learning and compare the results of the best-fitted machine learning (ML) model with a deep learning model, long short-term memory based on RNN (LSTM-RNN). RNN model has vanishing gradient issue, which slows down the training in the early layers of the network. LSTM-RNN is the advanced model which take care of vanishing gradient problem. The performance evaluation metric to compare the superiority of the model will be R2, mean square error (MSE), root means square error (RMSE), and mean absolute error (MAE).

scholarly journals Deep learning with long short-term memory networks and random forests for demand forecasting in multi-channel retail

2020 ◽  
Vol 58 (16) ◽  
pp. 4964-4979 ◽  
Author(s):  
Sushil Punia ◽  
Konstantinos Nikolopoulos ◽  
Surya Prakash Singh ◽  
Jitendra K. Madaan ◽  
Konstantia Litsiou
Keyword(s):  
Deep Learning ◽  
Random Forests ◽  
Short Term Memory ◽  
Demand Forecasting ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory
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