scholarly journals Adaptive IES Load Forecasting Method Based on the Octopus Model

2021 ◽  
Vol 9 ◽  
Author(s):  
Na Zhang ◽  
Xiao Pan ◽  
Yihe Wang ◽  
Mingli Zhang ◽  
Mengzeng Cheng ◽  
...  
Keyword(s):  
Adaptive Learning ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Load Forecasting ◽  
Energy System ◽  
Support Vector ◽  
Restricted Boltzmann Machines ◽  
Elman Neural Network ◽  
Forecasting Method ◽  
Integrated Energy System

Improving the accuracy and speed of integrated energy system load forecasting is a great significance for improving the real-time scheduling and optimized operation of the integrated energy system. In order to achieve rapid and accurate forecasting of the integrated energy system, this paper proposes an adaptive integrate energy system (IES) load forecasting method based on the octopus model. This method uses long short-term memory (LSTM), support vector machines (SVMs), restricted Boltzmann machines (RBMs), and Elman neural network as the octopus model quadrupeds. Through taking over differences in different data and training principles and utilizing the advantages of the octopus quadruped model, a special octopus-head and XGBoost algorithm were adopted to set the weight of the octopus’ quadruped and prevent local minimum points in the model. We train the octopus model through RMSProp adaptive learning algorithm, constrain the learning rate, get the best parameters, and improve the model’s adaptability to different types of data. In addition, for the incomplete comprehensive energy load data, the generative confrontation network is used to fill it. The simulation results show that compared with other prediction methods, the effectiveness and feasibility of the method proposed in this paper are verified.

scholarly journals Day-Ahead and Intra-Day Optimal Scheduling of Integrated Energy System Considering Uncertainty of Source & Load Power Forecasting

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2539
Author(s):  
Zhengjie Li ◽  
Zhisheng Zhang
Keyword(s):  
Wind Power ◽  
Demand Response ◽  
Solar Power ◽  
Load Forecasting ◽  
Energy System ◽  
Optimal Scheduling ◽  
Scheduling Scheme ◽  
Load Power ◽  
Integrated Energy System ◽  
Operation Cost

At present, due to the errors of wind power, solar power and various types of load forecasting, the optimal scheduling results of the integrated energy system (IES) will be inaccurate, which will affect the economic and reliable operation of the integrated energy system. In order to solve this problem, a day-ahead and intra-day optimal scheduling model of integrated energy system considering forecasting uncertainty is proposed in this paper, which takes the minimum operation cost of the system as the target, and different processing strategies are adopted for the model. In the day-ahead time scale, according to day-ahead load forecasting, an integrated demand response (IDR) strategy is formulated to adjust the load curve, and an optimal scheduling scheme is obtained. In the intra-day time scale, the predicted value of wind power, solar power and load power are represented by fuzzy parameters to participate in the optimal scheduling of the system, and the output of units is adjusted based on the day-ahead scheduling scheme according to the day-ahead forecasting results. The simulation of specific examples shows that the integrated demand response can effectively adjust the load demand and improve the economy and reliability of the system operation. At the same time, the operation cost of the system is related to the reliability of the accurate prediction of wind power, solar power and load power. Through this model, the optimal scheduling scheme can be determined under an acceptable prediction accuracy and confidence level.

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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