scholarly journals IMPROVING THE ALGORITHM FOR DETERMINING THE EFFECT OF AIR TEMPERATURE ON THE OVERALL ELECTRICAL LOAD OF THE POWER SYSTEM TO IMPROVE THE ACCURACY OF SHORT-TERM FORECASTING

2021 ◽  
Vol 2021 (2) ◽  
pp. 77-83
Author(s):  
P.O. Chernenko ◽  
◽  
V.V. Sychova ◽  
Keyword(s):  
Power System ◽  
Air Temperature ◽  
Temperature Sensors ◽  
Point Of View ◽  
Electrical Load ◽  
Short Term ◽  
Daily Schedule ◽  
Short Term Forecasting

Based on the research, the article presents three algorithms that allow to select from the overall electrical load (OEL) of the power system technological and temperature components in each hour of the daily schedule, which provides greater accuracy of short-term forecasting (STF) OEL of the power system. Calculations by three algorithms were performed according to Kyivenerho. The analysis of reading temperature sensors on four sources from the point of view of possibility of their application at STF is carried out. References 6, figures 3, tables 3.

MODELING AND SHORT-TERM FORECASTING OF TECHNOLOGY COMPONENT OF ELECTRICAL LOAD OF THE REGIONAL ELECTRIC POWER SYSTEM

2016 ◽  
Vol 2016 (4) ◽  
pp. 68-70 ◽  
Author(s):  
P. Chernenko ◽  
◽  
O. Martyniuk ◽  
V. Miroshnyk ◽  
◽  
...  
Keyword(s):  
Power System ◽  
Electric Power ◽  
Electric Power System ◽  
Electrical Load ◽  
Short Term ◽  
Short Term Forecasting

Jointly learning variational data assimilation models and solvers for geophysical dynamics

2021 ◽  
Author(s):  
Ronan Fablet ◽  
Bertrand Chapron ◽  
Lucas Drumetz ◽  
Etienne Memin ◽  
Olivier Pannekoucke ◽  
...  
Keyword(s):  
Neural Networks ◽  
Data Assimilation ◽  
Inverse Problems ◽  
Automatic Differentiation ◽  
Variational Data Assimilation ◽  
Point Of View ◽  
Variational Model ◽  
Short Term ◽  
End To End ◽  
Short Term Forecasting

<p>This paper addresses representation learning for the resolution of inverse problems  with geophysical dynamics. Among others, examples of inverse problems of interest include space-time interpolation, short-term forecasting, conditional simulation w.r.t. available observations, downscaling problems… From a methodological point of view, we rely on a variational data assimilation framework. Data assimilation (DA) aims to reconstruct the time evolution of some state given a series of  observations, possibly noisy and irregularly-sampled. Here, we investigate DA from a machine learning point of view backed by an underlying variational representation.  Using automatic differentiation tools embedded in deep learning frameworks, we introduce end-to-end neural network architectures for variational data assimilation. It comprises two key components: a variational model and a gradient-based solver both implemented as neural networks. A key feature of the proposed end-to-end learning architecture is that we may train the neural networks models using both supervised and unsupervised strategies. We first illustrate applications to the reconstruction of Lorenz-63 and Lorenz-96 systems from partial and noisy observations. Whereas the gain issued from the supervised learning setting emphasizes the relevance of groundtruthed observation dataset for real-world case-studies, these results also suggest new means to design data assimilation models from data. Especially, they suggest that learning task-oriented representations of the underlying dynamics may be beneficial. We further discuss applications to short-term forecasting and sampling design along with preliminary results for the reconstruction of sea surface currents from satellite altimetry data. </p><p>This abstract is supported by a preprint available online: https://arxiv.org/abs/2007.12941</p>

scholarly journals STUDI PERAMALAN BEBAN RATA – RATA JANGKA PENDEK MENGGUNAKAN METODA AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA

Sutet ◽  
2018 ◽  
Vol 7 (2) ◽  
pp. 93-101
Author(s):  
Redaksi Tim Jurnal
Keyword(s):  
Power Plants ◽  
Time Series Data ◽  
Moving Average ◽  
Current Model ◽  
Series Data ◽  
Electrical Load ◽  
Short Term ◽  
Autoregressive Integrated Moving Average ◽  
Independent Variable ◽  
Short Term Forecasting

Forecasting. Plans, power plants ,. Electricity needs are increasingly changing daily, so the State Electricity Company (PLN) as a provider of energy must be able to predict daily electricity needs. Short-term forecasting is the prediction of electricity demand for a certain period of time ranging from a few minutes to a week ahead. in shortterm electrical forecasting much of the literature describes the techniques and methods applied in forecasting, Autoregresive Integrated Moving Average (ARIMA), linear regression, and artificial intelligence such as Artificial Neural Networks and fuzzy logic. Short-term forecasting will be done by the authors using time series data that is the data of the use of electric power daily (electrical load) and ARIMA as a method of forecasting. ARIMA method or often called Box-Jenkins technique to find this method is suitable to predict variable costs quickly, simply, and cheaply because it only requires data variables to be predicted. ARIMA can only be used for short-term forecasting. ARIMA is a special linear test, in the form of forecasting this model is completely independent variable variables because this model uses the current model and past values of the dependent variable to produce an accurate short-term forecast.

scholarly journals A Hybrid Short-Term Load Forecasting Framework with an Attention-Based Encoder–Decoder Network Based on Seasonal and Trend Adjustment

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4612 ◽  
Author(s):  
Zhaorui Meng ◽  
Xianze Xu
Keyword(s):  
Power System ◽  
Load Forecasting ◽  
The United States ◽  
Attention Mechanism ◽  
Gradient Boosting ◽  
Percentage Error ◽  
Electrical Load ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Gated Recurrent Units

Accurate electrical load forecasting plays an important role in power system operation. An effective load forecasting approach can improve the operation efficiency of a power system. This paper proposes the seasonal and trend adjustment attention encoder–decoder (STA–AED), a hybrid short-term load forecasting approach based on a multi-head attention encoder–decoder module with seasonal and trend adjustment. A seasonal and trend decomposing technique is used to preprocess the original electrical load data. Each decomposed datum is regressed to predict the future electric load value by utilizing the encoder–decoder network with the multi-head attention mechanism. With the multi-head attention mechanism, STA–AED can interpret the prediction results more effectively. A large number of experiments and extensive comparisons have been carried out with a load forecasting dataset from the United States. The proposed hybrid STA–AED model is superior to the other five counterpart models such as random forest, gradient boosting decision tree (GBDT), gated recurrent units (GRUs), Encoder–Decoder, and Encoder–Decoder with multi-head attention. The proposed hybrid model shows the best prediction accuracy in 14 out of 15 zones in terms of both root mean square error (RMSE) and mean absolute percentage error (MAPE).

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