Ensemble approach for short term load forecasting in wind energy system using hybrid algorithm

2020 ◽  
Vol 11 (11) ◽  
pp. 5297-5314 ◽  
Author(s):  
Shweta Sengar ◽  
Xiaodong Liu
Keyword(s):  
Wind Energy ◽  
Hybrid Algorithm ◽  
Load Forecasting ◽  
Energy System ◽  
Short Term ◽  
Ensemble Approach ◽  
Wind Energy System ◽  
Short Term Load Forecasting

scholarly journals Optimized Operation of Integrated Energy Microgrid with Energy Storage Based on Short-Term Load Forecasting

Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 22
Author(s):  
Hanlin Dong ◽  
Zhijian Fang ◽  
Al-wesabi Ibrahim ◽  
Jie Cai
Keyword(s):  
Energy Storage ◽  
Optimization Technique ◽  
Load Forecasting ◽  
Energy System ◽  
Mixed Integer ◽  
Programming Approach ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Integrated Energy System ◽  
Optimal Coordination

This research proposes an optimization technique for an integrated energy system that includes an accurate prediction model and various energy storage forms to increase load forecast accuracy and coordinated control of various energies in the current integrated energy system. An artificial neural network is utilized to create an accurate short-term load forecasting model to effectively predict user demand. The 0–1 mixed integer linear programming approach is used to analyze the optimal control strategy for multiple energy systems with storage, cold energy, heat energy, and electricity to solve the problem of optimal coordination. Simultaneously, a precise load forecasting method and an optimal scheduling strategy for multienergy systems are proposed. The equipment scheduling plan of the integrated energy system of gas, heat, cold, and electricity is proposed after researching the operation characteristics and energy use process of the equipment in the combined power supply system. A system economic operation model is created with profit maximization in mind, while also taking into account energy coordination between energy and the power grid. The rationality of the algorithm and model is verified by analyzing the real data of a distributed energy station in Wuhan for two years.

Combination of short-term load forecasting models based on a stacking ensemble approach

2020 ◽  
Vol 216 ◽  
pp. 109921 ◽  
Author(s):  
Jihoon Moon ◽  
Seungwon Jung ◽  
Jehyeok Rew ◽  
Seungmin Rho ◽  
Eenjun Hwang
Keyword(s):  
Load Forecasting ◽  
Short Term ◽  
Forecasting Models ◽  
Ensemble Approach ◽  
Short Term Load Forecasting

An interval decomposition-ensemble approach with data-characteristic-driven reconstruction for short-term load forecasting

2022 ◽  
Vol 306 ◽  
pp. 117992
Author(s):  
Dongchuan Yang ◽  
Ju-e Guo ◽  
Shaolong Sun ◽  
Jing Han ◽  
Shouyang Wang
Keyword(s):  
Load Forecasting ◽  
Short Term ◽  
Ensemble Approach ◽  
Short Term Load Forecasting

Modeling of a simplified hybrid algorithm for short-term load forecasting in a power system network

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kathiresh Mayilsamy ◽  
Maideen Abdhulkader Jeylani A, ◽  
Mahaboob Subahani Akbarali ◽  
Haripranesh Sathiyanarayanan
Keyword(s):  
Time Series ◽  
Hybrid Algorithm ◽  
Hybrid Approach ◽  
Load Forecasting ◽  
Series Data ◽  
Percentage Error ◽  
Short Term ◽  
Content Type ◽  
Highly Nonlinear ◽  
Short Term Load Forecasting

Purpose The purpose of this paper is to develop a hybrid algorithm, which is a blend of auto-regressive integral moving average (ARIMA) and multilayer perceptron (MLP) for addressing the non-linearity of the load time series. Design/methodology/approach Short-term load forecasting is a complex process as the nature of the load-time series data is highly nonlinear. So, only ARIMA-based load forecasting will not provide accurate results. Hence, ARIMA is combined with MLP, a deep learning approach that models the resultant data from ARIMA and processes them further for Modelling the non-linearity. Findings The proposed hybrid approach detects the residuals of the ARIMA, a linear statistical technique and models these residuals with MLP neural network. As the non-linearity of the load time series is approximated in this error modeling process, the proposed approach produces accurate forecasting results of the hourly loads. Originality/value The effectiveness of the proposed approach is tested in the laboratory with the real load data of a metropolitan city from South India. The performance of the proposed hybrid approach is compared with the conventional methods based on the metrics such as mean absolute percentage error and root mean square error. The comparative results show that the proposed prediction strategy outperforms the other hybrid methods in terms of accuracy.

scholarly journals Short-term Forecast of Multiple Loads in Integrated Energy System Based on IPSO-WNN

2020 ◽  
Vol 194 ◽  
pp. 01029
Author(s):  
Cao Yuwei ◽  
Zeng Ming ◽  
Jiang Shigong ◽  
Yang Weihong ◽  
Shi Pengjia ◽  
...  
Keyword(s):  
Influencing Factors ◽  
Prediction Accuracy ◽  
Load Forecasting ◽  
Energy System ◽  
Optimal Operation ◽  
Selection Strategy ◽  
Forecasting Model ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Integrated Energy System

Accurate short-term energy load forecasting has a considerable influence on the economic scheduling and optimal operation of integrated energy system. This study proposes an improved particle swarm optimization-wavelet neural network (IPSO-WNN) method for short-term load forecasting of integrated energy system. First, Kendall rank correlation coefficient in Copula theory is used to analyze the correlation among the influencing factors, through which the influencing factors with strong correlation are selected as input variables of the model. Secondly, chaos algorithm and adaptive weight selection strategy are introduced in the POS-WNN forecasting model to improve the prediction accuracy. Therefore, a short-term load forecasting model of integrated energy system based on IPSO-WNN is established. Finally, the analysis of examples shows that the load prediction accuracy is significantly improved based on the IPSO-WNN model compared with the traditional forecasting model.

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