A Multi-Dimensional Method for Nodal Load Forecasting

2014 ◽  
Vol 1070-1072 ◽  
pp. 708-717
Author(s):  
Zhi Yuan Pan ◽  
Chao Nan Liu ◽  
Jing Wang ◽  
Yong Wang
Keyword(s):  
Energy Flow ◽  
Load Forecasting ◽  
Least Square ◽  
Support Vector ◽  
Forecasting Accuracy ◽  
Combination Forecasting ◽  
Forecasting Method ◽  
Load Pattern ◽  
And Control

The intelligent dispatch and control of future smart grid demands grasping of any nodal load pattern in the general great grid, therefore to realize the load forecasting of any nodal load is quite important. To solve this problem, focusing on overcoming the weakness of isolated nodal load forecasting and based on the correlation analysis, this paper proposes a multi-dimensional nodal load forecast system and corresponding method for effective prediction of any nodal load of the grid. This system includes topology partitioning of the grid energy flow according to layers and regions, basic forecasting unit composed of each layer’s total amount of load and its nodal loads, and combination forecasting for any node. The forecasting method is based on techniques including the multi-output least square support vector machine, Kalman filtering and the approximate optimal prediction. A case study shows that the multi-dimensional nodal load forecasting model helps to improve the forecasting accuracy, and has practical prospects.

APPLICATION OF A HYBRID OF LEAST SQUARE SUPPORT VECTOR MACHINE AND ARTIFICIAL BEE COLONY FOR BUILDING LOAD FORECASTING

2016 ◽  
Vol 78 (6-2) ◽  
Author(s):  
Mohammad Azhar Mat Daut ◽  
Mohammad Yusri Hassan ◽  
Hayati Abdullah ◽  
Hasimah Abdul Rahman ◽  
Md Pauzi Abdullah ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Hybrid Method ◽  
Artificial Bee Colony ◽  
Load Forecasting ◽  
Least Square ◽  
Electricity Production ◽  
Support Vector ◽  
Bee Colony ◽  
Forecasting Method ◽  
Building Load

Accurate load forecasting is an important element for proper planning and management of electricity production. Although load forecasting has been an important area of research, methods for accurate load forecasting is still scarce in the literature. This paper presents a study on a hybrid load forecasting method that combines the Least Square Support Vector Machine (LSSVM) and Artificial Bee Colony (ABC) methods for building load forecasting. The performance of the LSSVM-ABC hybrid method was compared to the LSSVM method in building load forecasting problems and the results has shown that the hybrid method is able to substantially improve the load forecasting ability of the LSSVM method.

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 Photovoltaic Power Generation Combination Forecasting Method Based on Similar Day and Cross Entropy Theory

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Qi Wang ◽  
Shunxiang Ji ◽  
Minqiang Hu ◽  
Wei Li ◽  
Fusuo Liu ◽  
...  
Keyword(s):  
Power Generation ◽  
Least Square ◽  
Cross Entropy ◽  
Support Vector ◽  
Forecasting Model ◽  
Entropy Theory ◽  
Short Term ◽  
Combination Forecasting ◽  
Combination Forecasting Model ◽  
Similar Day

The forecast for photovoltaic (PV) power generation is of great significance for the operation and control of power system. In this paper, a short-term combination forecasting model for PV power based on similar day and cross entropy theory is proposed. The main influencing factors of PV power are analyzed. From the perspective of entropy theory, considering distance entropy and grey relation entropy, a comprehensive index is proposed to select similar days. Then, the least square support vector machine (LSSVM), autoregressive and moving average (ARMA), and back propagation (BP) neural network are used to forecast PV power, respectively. The weights of three single forecasting methods are dynamically set by the cross entropy algorithm and the short-term combination forecasting model for PV power is established. The results show that this method can effectively improve the prediction accuracy of PV power and is of great significance to real-time economical dispatch.

Short-term load forecasting coupled with weather profile generation methodology

2017 ◽  
Vol 39 (3) ◽  
pp. 310-327 ◽  
Author(s):  
Guangya Zhu ◽  
Tin-Tai Chow ◽  
Norman Tse
Keyword(s):  
Input Data ◽  
Weather Prediction ◽  
Load Forecasting ◽  
Building Energy ◽  
Weather Data ◽  
Generation Model ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Building Load

Short-term building load forecasting is indispensable in daily operation of future intelligent/green buildings, particularly in formulating system control strategies and assessing the associated environmental impacts. Most previous research works have been focused on studying the advancement in forecasting techniques, but not as much on evaluating the availability of influential factors like the predicted weather profile in the coming hours. This article proposes an improved procedure to predict the building load 24 hours ahead, together with a backup weather profile generating method. The quality of the proposed weather profile generation model and the forecasting procedures were examined through a case study of application to university academic buildings. The results showed that the load forecasting accuracy with the application of either the real weather data on record or of the predicted weather data from the profile generation model is very much similar. This indicates that the weather prediction model is suitable for applying to building load forecasting. Besides, the comparisons between different sets of input data illustrated that the forecasting accuracy can be improved through the input data filtering and regrouping procedures. Practical application: A weather profile prediction technique for use in building energy forecasting was introduced. This can be coupled to a building energy use forecasting model for predicting the hourly consumption profile of the next day. This prediction time span can be crucial for formulating the daily operation plan of the utility systems or for smart micro-grid applications. The appropriateness of the methodology was evaluated through a case study.

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