scholarly journals Load Forecasting Analysis by Time Series Method

2018 ◽  
Vol 7 (2.4) ◽  
pp. 101
Author(s):  
Kuldeep S ◽  
Anitha G S
Keyword(s):  
Time Series ◽  
Power Systems ◽  
Load Forecasting ◽  
Economic Operation ◽  
Load Pattern ◽  
Time Series Method ◽  
Forecasting Analysis ◽  
And Behavior ◽  
Load Behavior ◽  
Tools And Methods

Load forecasting is a very important factor for designing power systems. A good knowledge of load pattern and behavior is very important for proper coordination, design and economic operation. Though a lot of research has been done on load forecasting, there are many tools and methods still being developed to accurately predict load behavior. This paper does an analysis of sample load data and predicts the next instant load using feedforward time series neural network model

A New Short-Term Load Forecasting in Power Systems

2014 ◽  
Vol 494-495 ◽  
pp. 1631-1635
Author(s):  
Hui Li ◽  
Hong Bin Sun
Keyword(s):  
Time Series ◽  
Power Systems ◽  
Load Forecasting ◽  
Reference Points ◽  
Optimization Approach ◽  
New Approach ◽  
Forecasting Accuracy ◽  
Power Load ◽  
Short Term Load Forecasting ◽  
Daily Load

For realizing highly accuracy load forecasting, a new method is proposed. Power load time series belongs to chaotic series. Firstly, for obtaining three parameters in chaotic theory, namely time delay, embedding dimension and the number of the nearest neighbors, self-correlation function method and G-P algorithm are used to reconstruct the phase space of chaotic time series. Secondly, ant colony optimization approach is introduced to more accurately acquire forecasting reference points, considering distance and relativity of phase points evolution in this paper. Finally, GM (1, 1) Model is applied to forecast daily load data. The actual forecasting results prove that the new approach has better forecasting accuracy and convergence.

scholarly journals ARIMA Models in Electrical Load Forecasting and Their Robustness to Noise

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7952
Author(s):  
Ewa Chodakowska ◽  
Joanicjusz Nazarko ◽  
Łukasz Nazarko
Keyword(s):  
Time Series ◽  
Power Systems ◽  
Moving Average ◽  
Arima Model ◽  
Load Forecasting ◽  
Model Identification ◽  
Electrical Load ◽  
Arima Models ◽  
Electrical Load Forecasting ◽  
The Impact

The paper addresses the problem of insufficient knowledge on the impact of noise on the auto-regressive integrated moving average (ARIMA) model identification. The work offers a simulation-based solution to the analysis of the tolerance to noise of ARIMA models in electrical load forecasting. In the study, an idealized ARIMA model obtained from real load data of the Polish power system was disturbed by noise of different levels. The model was then re-identified, its parameters were estimated, and new forecasts were calculated. The experiment allowed us to evaluate the robustness of ARIMA models to noise in their ability to predict electrical load time series. It could be concluded that the reaction of the ARIMA model to random disturbances of the modeled time series was relatively weak. The limiting noise level at which the forecasting ability of the model collapsed was determined. The results highlight the key role of the data preprocessing stage in data mining and learning. They contribute to more accurate decision making in an uncertain environment, help to shape energy policy, and have implications for the sustainability and reliability of power systems.

scholarly journals Load forecasting of refrigerated display cabinet based on CEEMD–IPSO–LSTM combined model

Open Physics ◽  
2021 ◽  
Vol 19 (1) ◽  
pp. 360-374
Author(s):  
Yuan Pei ◽  
Lei Zhenglin ◽  
Zeng Qinghui ◽  
Wu Yixiao ◽  
Lu Yanli ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
Load Forecasting ◽  
Series Data ◽  
Forecasting Model ◽  
Combined Model ◽  
Forecasting Accuracy ◽  
Forecasting Method ◽  
Consumption Reduction

Abstract The load of the showcase is a nonlinear and unstable time series data, and the traditional forecasting method is not applicable. Deep learning algorithms are introduced to predict the load of the showcase. Based on the CEEMD–IPSO–LSTM combination algorithm, this paper builds a refrigerated display cabinet load forecasting model. Compared with the forecast results of other models, it finally proves that the CEEMD–IPSO–LSTM model has the highest load forecasting accuracy, and the model’s determination coefficient is 0.9105, which is obviously excellent. Compared with other models, the model constructed in this paper can predict the load of showcases, which can provide a reference for energy saving and consumption reduction of display cabinet.

scholarly journals Short-term household load forecasting based on Long- and Short-term Time-series network

2021 ◽  
Vol 7 ◽  
pp. 58-64
Author(s):  
Xifeng Guo ◽  
Ye Gao ◽  
Yupeng Li ◽  
Di Zheng ◽  
Dan Shan
Keyword(s):  
Time Series ◽  
Load Forecasting ◽  
Short Term

scholarly journals Load Forecasting Based on LSTM Neural Network and Applicable to Loads of “Replacement of Coal with Electricity”

2021 ◽  
Author(s):  
Zexi Chen ◽  
Delong Zhang ◽  
Haoran Jiang ◽  
Longze Wang ◽  
Yongcong Chen ◽  
...  
Keyword(s):  
Power Systems ◽  
Short Term Memory ◽  
Load Capacity ◽  
Load Forecasting ◽  
Forecasting Method ◽  
Adequate Accuracy ◽  
Model Time Series ◽  
Power System Faults ◽  
Long Short Term Memory ◽  
Electric Loads

AbstractWith the complete implementation of the “Replacement of Coal with Electricity” policy, electric loads borne by urban power systems have achieved explosive growth. The traditional load forecasting method based on “similar days” only applies to the power systems with stable load levels and fails to show adequate accuracy. Therefore, a novel load forecasting approach based on long short-term memory (LSTM) was proposed in this paper. The structure of LSTM and the procedure are introduced firstly. The following factors have been fully considered in this model: time-series characteristics of electric loads; weather, temperature, and wind force. In addition, an experimental verification was performed for “Replacement of Coal with Electricity” data. The accuracy of load forecasting was elevated from 83.2 to 95%. The results indicate that the model promptly and accurately reveals the load capacity of grid power systems in the real application, which has proved instrumental to early warning and emergency management of power system faults.

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