Lightweight sustainable intelligent load forecasting platform for smart grid applications

Mukherjee, Amartya; Mukherjee, Prateeti; Dey, Nilanjan; De, Debashis; Panigrahi, B. K.

doi:10.1016/j.suscom.2019.100356

Cited by 22 publications

(8 citation statements)

References 12 publications

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“…The most protruding renewable energy source is solar energy 7 . The other important sources are hydropower, wind power, biomass power, ocean waves, and geothermal energy 8 . Figure 1 shows the consumers in the smart grid.…”

Section: Outline About the Renewable Energy Sourcesmentioning

confidence: 99%

Machine learning‐based energy efficient technologies for smart grid

Yao

Li²,

Zuo³

et al. 2021

Int Trans Electr Energ Syst

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The smart grid will allow substantial electricity savings and peak demand savings by potentially supplying utility power for direct load management, the calculation in support of competitive pricing, and even the granular data required for energy usage to be more targeted explicitly at customer needs, the processing of data and predictions for a smart grid in a building with the energy profile and occupants' profile, is challenging. This article has been suggested a Machine Learning-Based Energy-Efficient Framework to analyze the motion of occupants, deliver short-term energy forecasts, and assign renewable energy in the smart grid. Second, an indoor localization device with wireless data analysis collects occupants' profile, and the energy profile is managed by a real-time smart meter network with an electrical charge evaluation. Furthermore, the energy profile and 24-hour profile will be paired with a forecast utilizing an online machine learning framework with an analysis of data in real-time. To decrease peak demand for the primary power grid, the solar energy source is assigned to the further power grids based upon the forecast occupant movement profile and energy consumption profile. On the smart gateway network, the complete power flow with minimal computing resources and a general enabled engine can be controlled. The results of the studies on real-time datasets show that the precision of the suggested energy forecast will increase significantly when compared to the other existing methods.

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Section: Outline About the Renewable Energy Sourcesmentioning

confidence: 99%

Machine learning‐based energy efficient technologies for smart grid

Yao

Li²,

Zuo³

et al. 2021

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

show abstract

“…During the past 10 years, with the rapid development of artificial intelligence and deep learning technology, datadriven deep learning models [45] have become very popular and have been widely used in engineering [17], [18] electricity [13], [16] agriculture [11] and many other fields. Chatterjee et al [17] proposed a method based on particle swarm optimization to train the NN (NN-PSO), which can solve the problem of predicting the failure of multistoried reinforced concrete buildings by detecting the failure probability of the multistoried RC building structures in the future.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The prediction effect will gradually decline with the extension of the prediction period [12], which limits the application of the model to a certain extent. Some recent studies have shown that nontime series deep learning methods [13], such as GBDT [14], [15], SVM, random forest [16] and neural network [17], [18], exhibit good forecasting effects in some forecasting fields, but the prediction of water accumulation processes is a continuous process that changes over time. These nontime series models can only predict a certain feature of the water accumulation process and cannot realize the prediction of the water accumulation process.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Prediction of the Water Accumulation Process of Urban Stormy Accumulation Points Based on Deep Learning

Zhou

Wang

2020

IEEE Access

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Influenced by climate change and urbanization, urban flood frequently occurs and represents a serious challenge for many cities. Therefore, it is necessary to generate refined predictions of urban floods, such as the prediction of water accumulation processes at water accumulation points, which is of great significance for supporting water-related managers to reduce flood losses. In this study, 16 combination schemes of rainfall sensitivity indicators were used to determine the optimal scheme for predicting the depth of accumulated water, and the gradient boosting decision tree (GBDT) algorithm in deep learning was used to build a prediction model of the accumulation process of urban stormy accumulation points. Among the 16 schemes, the relative error of scheme 1 is 15.39%, and the qualified rate is 92.86%. This scheme exhibits the highest accuracy for the prediction results of water accumulation depth. Given this finding, the GBDT algorithm was used to construct a regression prediction model of the water accumulation process based on the collected historical rainfall water accumulation data of 50 water accumulation points. The results demonstrated that the GBDT regression prediction model has a mean relative error of 19.77%, a qualified rate of 82.00%, and a peak average relative error of 5.48%, which verify the validity and applicability of the model for the real-time prediction of the process of water accumulation. INDEX TERMS Urban flood, Deep learning, Water accumulation, Real-time prediction.

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“…The ML algorithm collects information from the IoT devices and analysis the load and forecasting information. The ML and IoT embedded algorithms takes lower processing price and produce highest accuracy rate [16]. Through these analyses, the ML algorithms play a vital role in SG is identified.…”

Section: Introductionmentioning

confidence: 99%

Random Forest Based Power Sustainability and Cost Optimization in Smart Grid

Durairaj¹,

Wróblewski²,

Sheela³

et al. 2022

Preprint

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Presently power control and management play a vigorous role in information technology and power management. Instead of non-renewable power manufacturing, renewable power manufacturing is preferred by every organization for controlling resource consumption, price reduction and efficient power management. Smart grid efficiently satisfies these requirements with the integration of machine learning algorithms. Machine learning algorithms are used in a smart grid for power requirement prediction, power distribution, failure identification etc. The proposed Random Forest-based smart grid system classifies the power grid into different zones like high and low power utilization. The power zones are divided into number of sub-zones and map to random forest branches. The sub-zone and branch mapping process used to identify the quantity of power utilized and the non-utilized in a zone. The non-utilized power quantity and location of power availabilities are identified and distributed the required quantity of power to the requester in a minimal response time and price. The priority power scheduling algorithm collect request from consumer and send the request to producer based on priority. The producer analysed the requester existing power utilization quantity and availability of power for scheduling the power distribution to the requester based on priority. The proposed Random Forest based sustainability and price optimization technique in smart grid experimental results are compared to existing machine learning techniques like SVM, KNN and NB. The proposed random forest-based identification technique identifies the exact location of the power availability, which takes minimal processing time and quick responses to the requestor. Additionally, the smart meter based smart grid technique identifies the faults in short time duration than the conventional energy management technique is also proven in the experimental results.

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Lightweight sustainable intelligent load forecasting platform for smart grid applications

Cited by 22 publications

References 12 publications

Machine learning‐based energy efficient technologies for smart grid

Machine learning‐based energy efficient technologies for smart grid

Real-Time Prediction of the Water Accumulation Process of Urban Stormy Accumulation Points Based on Deep Learning

Random Forest Based Power Sustainability and Cost Optimization in Smart Grid

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