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An Ensemble Model Based on Machine Learning Methods for Short-term Power Load Forecasting

Liqiang Ren, Limin Zhang, Haipeng Wang, Lin Qi
2018 IOP Conference Series: Earth and Environment  
Given the significant fluctuation of errors for single forecasting model and limitation of linear combined forecasting models, A nonlinear multi-model ensemble method for short-term power load forecasting  ...  On this basis, three kinds of single prediction models of random forest, support vector machine and Xgboost are modelled, and three different prediction results are obtained.  ...  Conclusion A nonlinear multi-model ensemble method for short-term power load forecasting is proposed.  ... 
doi:10.1088/1755-1315/186/5/012042 fatcat:uk6feol47jg4nkzvy674k7372e

A Study on Load Forecasting of Distribution Line Based on Ensemble Learning for Mid- to Long-Term Distribution Planning

Jintae Cho, Yeunggul Yoon, Yongju Son, Hongjoo Kim, Hosung Ryu, Gilsoo Jang
2022 Energies  
This paper analyzed the existing mid-to-long-term load forecasting method for KEPCO's distribution planning and proposed a mid- to long-term load forecasting method based on ensemble learning.  ...  of ensemble load forecast models.  ...  While the short-term load forecasting of distribution lines is necessary for operating distribution systems, the mid-to long-term load forecasting provides very important information for operation, planning  ... 
doi:10.3390/en15092987 fatcat:bagsjibx5fe4dkq2vlaodeinly

Deep forest regression for short-term load forecasting of power systems

Linfei Yin, Zhixiang Sun, Fang Gao, Hui Liu
2020 IEEE Access  
Then, the deep forest regression is applied into the short-term load forecasting of power systems.  ...  To mitigate the adverse effect of the hyper-parameters for the deep learning algorithms, this paper proposes deep forest regression for the short-term load forecasting of power systems.  ...  CONCLUSION AND PROSPECT The deep forest regression is proposed for short-term load forecasting of power systems in this paper.  ... 
doi:10.1109/access.2020.2979686 fatcat:2beatb6psvd4rgia5q6egjavnq

Short-Term Load Forecasting Using Random Forests [chapter]

Grzegorz Dudek
2015 Advances in Intelligent Systems and Computing  
This study proposes using a random forest model for short-term electricity load forecasting.  ...  As an illustration, the proposed forecasting model is applied to historical load data in Poland and its performance is compared with some alternative models such as CART, ARIMA, exponential smoothing and  ...  Introduction Short-term load forecasting (STLF) is necessary for economic power generation and system security.  ... 
doi:10.1007/978-3-319-11310-4_71 fatcat:lmjiqyl6nbcfpoeja4joypem6u

Short-Term Energy Forecasting Using Machine-Learning-Based Ensemble Voting Regression

Pyae-Pyae Phyo, Yung-Cheol Byun, Namje Park
2022 Symmetry  
Accordingly, electric industries have paid attention to short-term energy forecasting to assist their management system.  ...  This improved performance shows that our approach is promising for symmetrical forecasting using time series energy data in the power system sector.  ...  Forecasting can be divided into three terms based on predictive duration: short term, medium term, and long term [2] .  ... 
doi:10.3390/sym14010160 fatcat:2rlzzo4qsbel7l445vwtviyxdu

Analysis of Electric Load Forecasting using Artificial Intelligence

Srashti Shrivastava, Dr. Krishna Teerth Chaturvedi
, transmission, distribution and energy markets. short-term load forecasts play an important role in the operation of power systems to ensure an immediate balance between energy production and demand.  ...  as compared to other artificial intelligence algorithms for 24 hours load forecasting as well as for 7 day load forecasting.  ...  Junran Peng [9] used an adaptive network-based fuzzy inference system (ANFIS) model to construct the short-term load forecasting model based on factors such as weather and date types, etc.  ... 
doi:10.24113/ijoscience.v4i5.137 fatcat:ytuvjthyzzexrgkjneks4wtctm

Segmenting Residential Smart Meter Data for Short-Term Load Forecasting

Alexander Kell, A. Stephen McGough, Matthew Forshaw
2018 Proceedings of the Ninth International Conference on Future Energy Systems - e-Energy '18  
KEYWORDS Advanced metering infrastructure (AMI), k-means clustering, neural networks, random forest, support vector regression, smart meter, load profiles, electrical power load forecasting, short-term  ...  Short-term load forecasting is utilised in both real-time scheduling of electricity, and load-frequency control.  ...  Random Forests are an ensemble-based learning method for classification and regression, and are made up of many decision trees.  ... 
doi:10.1145/3208903.3208923 dblp:conf/eenergy/KellMF18 fatcat:y4rpwvp52nbspnmxw4shnd4hqi

Load Forecasting for a Campus University Using Ensemble Methods Based on Regression Trees

María Ruiz-Abellón, Antonio Gabaldón, Antonio Guillamón
2018 Energies  
The purpose of the present study is to evaluate the effectiveness of using ensemble methods based on regression trees in short-term load forecasting.  ...  To illustrate this task, four methods (bagging, random forest, conditional forest, and boosting) are applied to historical load data of a campus university in Cartagena (Spain).  ...  Authors have also received funds from these grants for covering the costs to publish in open access. Conflicts of Interest: The authors declare no conflict of interest. Energies 2018, 11, 2038  ... 
doi:10.3390/en11082038 fatcat:mx6k6ylntfaulkinm34pi7g2fe

Application of Deep Neural Network Ensemble in a Problem of Short-Term Load Forecasting Guaranteed Electricity Supplier
Применение ансамбля глубоких нейронных сетей в задачах краткосрочного прогнозирования почасового электропотребления гарантирующего поставщика электроэнергии

Nikolay Serebryakov, Polzunov Altai State Technical University
2021 Electrotechnical Systems and Complexes  
Application of Deep Neural Network Ensemble in a Problem of Short-Term Load Forecasting Guaranteed Electricity Supplier.  ...  Modification of random forest based approach for streaming data with concept drift. Vestnik YuUrGU. Seriya "Matematicheskoe modelirovanie i programmirovanie.  ... 
doi:10.18503/2311-8318-2021-2(51)-52-60 fatcat:ghp7cxpm4bh3tkwd3cnz5fl7pq

Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting

Federico Divina, Aude Gilson, Francisco Goméz-Vela, Miguel García Torres, José Torres
2018 Energies  
To this aim, in this paper we propose a strategy based on ensemble learning in order to tackle the short-term load forecasting problem.  ...  More importantly, this case study has shown that using an ensemble scheme can achieve very accurate predictions, and thus that it is a suitable approach for addressing the short-term load forecasting problem  ...  Short-term load forecasting is an important problem.  ... 
doi:10.3390/en11040949 fatcat:awrql7giova45jdkpwpzpzfc7q

Peak-Load Forecasting for Small Industries: A Machine Learning Approach

Dong-Hoon Kim, Eun-Kyu Lee, Naik Bakht Sania Qureshi
2020 Sustainability  
Peak-load forecasting prevents energy waste and helps with environmental issues by establishing plans for the use of renewable energy. For that reason, the subject is still actively studied.  ...  On the basis of the pattern of load information of most industrial facilities, new features were selected, and a generalized model was developed through the aggregation of ensemble models.  ...  That paper focused on a deep learning-based method for load forecasting, as the authors presented novel energy load forecasting methodology based on deep neural networks, specifically long short-term memory  ... 
doi:10.3390/su12166539 fatcat:znys4pj4frcjdcgh5jjwjw2uhe

Ensembling methods for countrywide short term forecasting of gas demand [article]

Emanuele Fabbiani, Andrea Marziali, Giuseppe De Nicolao
2020 arXiv   pre-print
Nine "base forecasters" are implemented and compared: Ridge Regression, Gaussian Processes, Nearest Neighbours, Artificial Neural Networks, Torus Model, LASSO, Elastic Net, Random Forest, and Support Vector  ...  We found that ensemble predictors perform consistently better than base ones. Moreover, our models outperformed Transmission System Operator (TSO) predictions in a two-year out-of-sample validation.  ...  Several forecasting models were investigated and compared: nine base models plus four ensemble models. Aggregated models were found to be consistently more effective than base ones.  ... 
arXiv:1902.00097v3 fatcat:4tsdovai7fddlchk7sux33fjdy

Short Term Load Forecasting using Regression Trees: Random Forest, Bagging and M5P

Ankit Kumar Srivastava
2020 International Journal of Advanced Trends in Computer Science and Engineering  
Decision making in the energy market has to be based on accurate forecasts of the load demand. Therefore, Short Term Load Forecasting (STLF) is important tools in the energy market.  ...  In this paper, load forecasting using regression tree methods (Random Forest, Bagging and M5P) are used to effectively forecast the load.  ...  INTRODUCTION The term short-term load forecasting (STLF) is a process to estimate the load over an interval ranging from hours to week.  ... 
doi:10.30534/ijatcse/2020/152922020 fatcat:t7lmrcim2za2pcpfdx2y3bic5u

Short- and Very Short-Term Firm-Level Load Forecasting for Warehouses: A Comparison of Machine Learning and Deep Learning Models

Andrea Maria N. C. Ribeiro, Pedro Rafael X. do Carmo, Patricia Takako Endo, Pierangelo Rosati, Theo Lynn
2022 Energies  
The proposed XGBoost models outperformed other models for both very short-term load forecasting (VSTLF) and short-term load forecasting (STLF); the ARIMA model performed the worst.  ...  Networks (RNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)), and a classical time series model, Autoregressive Integrated Moving Average (ARIMA) for predicting daily energy consumption  ...  Random Forest models are based on the Bagging ensemble learning theory [64] and the random subspace method [65] .  ... 
doi:10.3390/en15030750 fatcat:mmpxb5qy6na4jp2prrn3cnl3vq

Artificial Intelligence Techniques in Smart Grid: A Survey

Olufemi A. Omitaomu, Haoran Niu
2021 Smart Cities  
This survey presents a structured review of the existing research into some common AI techniques applied to load forecasting, power grid stability assessment, faults detection, and security problems in  ...  It also provides further research challenges for applying AI technologies to realize truly smart grid systems. Finally, this survey presents opportunities of applying AI to smart grid problems.  ...  Based on the data provided by smart meters, many techniques are proposed and applied for power system LF. Short-Term Load Forecasting Qiu et al.  ... 
doi:10.3390/smartcities4020029 doaj:85074e6b64e546c8b5b61351aad66daa fatcat:2n46ot2yeveuxitvrfce6drne4
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