Short-term Power Demand Forecasting using Fixed Information at the Time of Prediction

2017 ◽  
Vol 137 (8) ◽  
pp. 1036-1042
Author(s):  
Ken-ihi Tokoro ◽  
Takayuki Higo
Keyword(s):  
Demand Forecasting ◽  
Power Demand ◽  
Short Term

Ultra-Short-Term Industrial Power Demand Forecasting Using LSTM Based Hybrid Ensemble Learning

2020 ◽  
Vol 35 (4) ◽  
pp. 2937-2948 ◽  
Author(s):  
Mao Tan ◽  
Siping Yuan ◽  
Shuaihu Li ◽  
Yongxin Su ◽  
Hui Li ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Demand Forecasting ◽  
Power Demand ◽  
Short Term

Building power demand forecasting

2016 ◽  
Vol 58 (1) ◽  
Author(s):  
Oleg Valgaev ◽  
Friederich Kupzog
Keyword(s):  
Demand Response ◽  
Renewable Energy Sources ◽  
Demand Forecasting ◽  
Power Demand ◽  
Distribution Grid ◽  
Demand Forecast ◽  
Short Term ◽  
Flexible Loads ◽  
Short Term Forecasting ◽  
Pv Generation

AbstractBuildings acting as flexible loads have been often proposed to mitigate the volatility of renewable energy sources. Thereby, an accurate short-term demand forecast is indispensable for effective demand side management. At the same time, standardized load profiles, commonly used in the distribution grid, are inadequate for load forecasting within building domain. For this PhD, project a novel short-term forecasting model is proposed for that domain. It considers not only residual load, but also scheduled demand response as well as the PV-generation of the building. Moreover, it is not building specific and is, therefore, suitable for area-wide application within building domain.

A novel multi-time-scale modeling for electric power demand forecasting: From short-term to medium-term horizon

2017 ◽  
Vol 142 ◽  
pp. 58-73 ◽  
Author(s):  
Kianoosh G. Boroojeni ◽  
M. Hadi Amini ◽  
Shahab Bahrami ◽  
S.S. Iyengar ◽  
Arif I. Sarwat ◽  
...  
Keyword(s):  
Electric Power ◽  
Time Scale ◽  
Demand Forecasting ◽  
Power Demand ◽  
Medium Term ◽  
Short Term ◽  
Scale Modeling

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 Very short-term forecasting of power demand of big dynamics objects

2019 ◽  
Vol 84 ◽  
pp. 01007
Author(s):  
Mirosław Parol ◽  
Paweł Piotrowski ◽  
Mariusz Piotrowski
Keyword(s):  
Low Voltage ◽  
Renewable Energy Sources ◽  
Demand Forecasting ◽  
Power Demand ◽  
Short Term ◽  
Forecasting Methods ◽  
Ex Post ◽  
The Subject ◽  
Short Term Forecasting ◽  
Micro Systems

The issue of very short-term forecasting is gaining more and more importance. It covers both the subject of power demand forecasting and forecasting of power generated in renewable energy sources. In particular, for the reason of necessity of ensuring reliable electricity supplies to consumers, it is very important in small energy micro-systems, which are commonly called microgrids. Statistical analysis of data for a sample big dynamics low voltage object will be presented in this paper. The object, in paper author’s opinion, belongs to a class of objects with difficulties in forecasting, in case of very short-term horizon. Moreover, forecasting methods, which can be applied to this type of forecasts, will be shortly characterized. Then results of sample very short-term ex post forecasts of power demand provided by several selected forecasting methods will be presented, as well as some qualitative analysis of obtained forecasts will be carried out. At the end of the paper observations and conclusions concerning analyzed subject, i.e. very short-term forecasting of power demand of big dynamics objects, will be presented.

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.

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