A Prediction Model of Power Consumption in Smart City Using Hybrid Deep Learning Algorithm
JOIV: International Journal on Informatics Visualization, 2023 A smart city utilizes vast data collected through electronic methods, such as sensors and cameras, to improve daily life by managing resources and providing services. Moving towards a smart grid is a step in realizing this concept.
Salam Abdulkhaleq Noaman +2 more
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Support Vector Machine to Predict Electricity Consumption in the Energy Management Laboratory
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 2021 Predicted electricity consumption is needed to perform energy management. Electricity consumption prediction is also very important in the development of intelligent power grids and advanced electrification network information.
Azam Zamhuri Fuadi +2 more
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Improved terrain estimation from spaceborne lidar in tropical peatlands using spatial filtering
Science of Remote Sensing, 2023 Tropical peatlands are estimated to hold carbon stocks of 70 Pg C or more as partly decomposed organic matter, or peat. Peat may accumulate over thousands of years into gently mounded deposits called peat domes with a relief of several meters over ...
Alexander R. Cobb +6 more
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A Comparison of Root Mean Square Errors on Skeletonization Methods [PDF]
International Journal of Applied Physics and Mathematics, 2012 Vectorization is the most fundamental operation in interpretation of line drawings and document analysis. There are several reasons for converting image vectorization. Vector data is normally created from existing natural source image like photographs, scanned images.
Neeti Daryal, Vinod Kumar
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Root-mean-squared deviation for 6-AIH+ (cc-pVDZ)
, 2021 This dataset contains the root-mean-squared deviation (RMSD) calculated between the geometries collected from each frame of the NAMD simulation and the equilibrium geometries at the minimum of each surface state (S0, S1, S2 and S3).
Saikat Mukherjee (10824507) +2 more
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Root-mean-squared deviation for 7-AIH+ (cc-pVDZ)
, 2021 This dataset contains the root-mean-squared deviation (RMSD) calculated between the geometries collected from each frame of the NAMD simulation and the equilibrium geometries at the minimum of each surface state (S0, S1, S2 and S3).
Saikat Mukherjee (10824507) +2 more
core +1 more source
Investigation of the efficiency of support vector machine in predicting changes in water quality parameters (Case study: Choghakhor International Wetland) [PDF]
بومشناسی آبزیان, 2020 Inland waters, such as wetlands, are considered to be sensitive ecosystems, and sustainable productivity can only be achieved by adopting an appropriate environmental approach.
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Moments and root-mean-square error of the Bayesian MMSE estimator of classification error in the Gaussian model [PDF]
Pattern Recognition, 2014 The most important aspect of any classifier is its error rate, because this quantifies its predictive capacity. Thus, the accuracy of error estimation is critical. Error estimation is problematic in small-sample classifier design because the error must be estimated using the same data from which the classifier has been designed. Use of prior knowledge,
Amin Zollanvari, Edward R. Dougherty
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Forecasting Wind Energy Production Using Machine Learning Techniques [PDF]
E3S Web of Conferences, 2023 Wind energy is an essential source of renewable energy that has gained popularity in recent years. Accurately forecasting wind energy production is crucial for efficient energy management and distribution.
Margarat G. Simi +3 more
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, 2023 Mean absolute error (MAE), R squared, root mean squared error (RMSE), symmetric mean absolute percentage error (SMAPE) for train, test & validation data.
Swapnashree Satapathy (15426889) +9 more
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