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Improving clustering based forecasting of aggregated distribution transformer loadings with gradient boosting and feature selection
2021
IEEE Access
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Third, feature selection is applied to improve the forecast accuracy of each cluster. Finally, the day-ahead load forecast of all clusters are aggregated. ...
Second, a gradient boosting algorithm is used to forecast the load of each cluster and calculate the related feature importance. ...
FEATURE SELECTION As explained, (gradient) boosting algorithms based on DT have the advantage over other ML algorithms to interpret the correlation between the applied features and forecasted load based ...
doi:10.1109/access.2021.3137870
fatcat:4khxiegs3nfmlberwu6h6ozqvi
HSIC Bottleneck based Distributed Deep Learning Model for Load Forecasting in Smart Grid with A Comprehensive Survey
2020
IEEE Access
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relevance and
redundancy of feature selection
in MTLF
and improves the
forecasting validity ...
2) Feature extraction
3) Probability
distribution
Establishing LRD with CNN to
improve
the
probability
distribution
based
load
forecasting
and
feature
extraction ...
CONFLICTS OF INTEREST The authors declare no conflict of interest. ...
doi:10.1109/access.2020.3040083
fatcat:tsqokovkm5gpfdsnm7bph73piu
Peak-Load Forecasting for Small Industries: A Machine Learning Approach
2020
Sustainability
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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. ...
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. ...
Some studies evaluated special topics such as feature selection, feature extraction, similarity determination, and clustering that indirectly improve both time-series analysis and AI techniques, instead ...
doi:10.3390/su12166539
fatcat:znys4pj4frcjdcgh5jjwjw2uhe
Review of Low Voltage Load Forecasting: Methods, Applications, and Recommendations
[article]
2021
arXiv
pre-print
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Reliable forecasting will be a necessary component of many of these systems to anticipate key features and uncertainties. ...
Another aim of this paper is to facilitate the continued improvement and advancement in this area. To this end, the paper also surveys some of the most relevant and promising trends. ...
Further, load profile clustering can be used to improve an aggregate forecast. ...
arXiv:2106.00006v2
fatcat:rb2yrt4tsjap3jhrb7dz76dg2e
Artificial Intelligence and Design of Experiments for Assessing Security of Electricity Supply: A Review and Strategic Outlook
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2112.04889v1 fatcat:rm5z3a7mr5avtidu7fbxpwnah4
Among other findings, we identify metamodeling of complex security of electricity supply models using AI methods and applications of AI-based methods for forecasts of storage dispatch and (non-)availabilities ...
For this, we conduct a large-scale quantitative review on selected fields of application and methods and make a synthesis that relates the different disciplines to each other. ...
Acknowledgements This research was funded by the German Federal Ministry for Economic Affairs and Energy (BMWi) within the project KIVi (grant ID:0 3EI1022A). ...
arXiv:2112.04889v2
fatcat:zfm7gfnfcraoplytbaixsdx2ia
Performance Evaluation of Distributed Machine Learning for Load Forecasting in Smart Grids
2020
2020 Cybernetics & Informatics (K&I)
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Load forecasting in smart grid is the process of predicting the amount of electrical power to meet the short, medium and long term demands. ...
Using MLib allows testing the classic regression algorithms such as linear regression, generalized linear regression, decision tree, random forest and gradient-boosted trees in addition to survival regression ...
In [4] , Dong et al. worked with the clustering of the load datasets based on the k-means clustering algorithm. ...
doi:10.1109/ki48306.2020.9039797
fatcat:qdjyvyfn75fwlgvxhj5i3wok3a
Short-Term Load Forecasting Based on the Transformer Model
2021
Information
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From the perspective of energy providers, accurate short-term load forecasting plays a significant role in the energy generation plan, efficient energy distribution process and electricity price strategy ...
To handle these challenges, in this work, we introduce a novel model based on the Transformer network to provide an accurate day-ahead load forecasting service. ...
We employed a novel boosting decision tree algorithm named light gradient boosting machine (LightGBM) to calculate the weight parameter and k-means cluster algorithm to deal with the similar day selection ...
doi:10.3390/info12120516
fatcat:ozypvifl7bco7dshz5c4yteaii
Day-Ahead Electric Load Forecasting for the Residential Building with a Small-Size Dataset Based on a Self-Organizing Map and a Stacking Ensemble Learning Method
2019
Applied Sciences
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Recent increases in the adoption of advanced metering infrastructure (AMI) have made building electrical consumption data available, and this has increased the feasibility of data-driven load forecasting ...
Using a clustering technique on small datasets could lead to overfitting when using forecasting models following an SOM network to be trained with clusters. ...
This may include administrative and technical support, or donations in kind (e.g., materials used for experiments).
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/app9061231
fatcat:72nxwuycnrccfkldpport4nuxa
Electrical peak demand forecasting- A review
[article]
2021
arXiv
pre-print
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Thirdly, a comparative analysis of peak load forecast methods are summarized and different optimizing methods to improve the forecast performance are discussed. ...
renewable energy at both transmission and distribution level, which characterizes the peak load demand with stronger randomness and less predictability and therefore poses a threat to the power grid security ...
Commonly used boosting algorithms in the reviewed papers are adaptive boosting (AdaBoost), boosting tree, gradient boosting (GB) and Extreme gradient boosting (XGBoost). ...
arXiv:2108.01393v1
fatcat:2egx6ozkqzgvpfamdtpd6r2d4a
Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges
2018
IEEE Transactions on Smart Grid
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Following the three stages of analytics, namely, descriptive, predictive and prescriptive analytics, we identify the key application areas as load analysis, load forecasting, and load management. ...
How to employ massive smart meter data to promote and enhance the efficiency and sustainability of the power grid is a pressing issue. ...
Combining the gradient boosting method and quantile regression, a boosting additive quantile regression method was proposed in [32] to quantify the uncertainty and generate probabilistic forecasts. ...
doi:10.1109/tsg.2018.2818167
fatcat:yacc5fol6vhydkw2azgy4mpfai
Multi-Timescale Forecasting of Battery Energy Storage State-of-Charge under Frequency Containment Reserve for Normal Operation
2019
2019 16th International Conference on the European Energy Market (EEM)
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The battery state-of-charge data for the performance evaluation were simulated with a droop curve battery model based on the historical frequency data in the northern Europe synchronous area. ...
Forecasting the state-of-charge changes of battery energy storage, anticipated from a provision of different services, can facilitate planning of its market participation strategy and leverage the maximum ...
Light Gradient Boosting Machine Light Gradient Boosting Machine is a modern and fast gradient boosting framework that is also based on the DT algorithm [9] . ...
doi:10.1109/eem.2019.8916335
fatcat:w3ctsg6c7bhldbsbr2vnkc7fum
Guest Editorial for the Special Section on Advances in Renewable Energy Forecasting: Predictability, Business Models and Applications in the Power Industry
2022
IEEE Transactions on Sustainable Energy
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They introduce a novel ensemble forecasting method based on model aggregation and feature engineering, adaptive error-based combination weights and forgetting factors. ...
IMPROVE PREDICTABILITY WITH MODEL ENSEMBLE Nejati and Amjady present a model-agnostic hybrid classification-regression method for short-term point solar energy forecasting that i) uses a feature selecting ...
doi:10.1109/tste.2022.3157009
fatcat:opza5jppwbcb5ggbshlf72jqt4
An Ensemble Energy Consumption Forecasting Model Based on Spatial-Temporal Clustering Analysis in Residential Buildings
2021
Energies
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First, the apartment-level historical electric consumption data is clustered. Later the clusters are aggregated based on consumption profiles of consumers. ...
The proposed ensemble forecasting scheme is well suited for predictive modeling and short-term load forecasting. ...
Conflicts of Interest: The authors declare no conflict of interest regarding the publication of this paper. ...
doi:10.3390/en14113020
fatcat:a5bj4gvcvvb7vedq33giyzau3a
Guest Editorial: Advanced Data-Analytics for Power System Operation, Control and Enhanced Situational Awareness
2020
IET Generation, Transmission & Distribution
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The method sets the balance factor and modulating factor in the loss function of light gradient boosting machine (LightGBM). ...
Lai, et al., in their paper 'Load forecasting based on deep neural network and historical data augmentation', present a load forecasting method known as deep neural network and historical data augmentation ...
Her research interests include microgrid operation and planning, integration of distributed resources, data-driven energy storage system modelling, planning and control. ...
doi:10.1049/iet-gtd.2020.1493
fatcat:ern3em3movepzk6j33ohqlcndy
Forecasting Peak and Appliance Level Demand Using Smart Meter Data
2021
Zenodo
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The hybrid MLP model with k-means clustering performs at accuracy in the range 84-99%. ...
With this prediction, users can decide on the usage of appliances to manage electricity consumption and hence reduce the bill. ...
Machine learning models like Extreme Gradient boosting (XGBoost), Categorical boosting, and light gradient boosting are applied on smart meter data to inspect the power theft problems which can cause a ...
doi:10.5281/zenodo.4925862
fatcat:gzmaulqz65bbnekpzsw3t6rc3a
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