Zettelkasten/Terminology Information95

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  GWO (Gray Wolf Optimization) Information The GWO (Gray Wolf Optimization) algorithm is an optimization algorithm inspired by the social hunting behavior of grey wolves in the wild. The algorithm involves a population of candidate solutions (or "wolves") that are initialized randomly in the search space. Each wolf in the population represents a potential solution to the optimization problem being solved. The algorithm iterat.. Zettelkasten/Terminology Information 2023. 3. 6.

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  LM (Levenberg–Marquardt) Information LM (Levenberg-Marquardt) is an optimization algorithm used in nonlinear least squares regression. LM is a modification of the Gauss-Newton algorithm, which is used to solve nonlinear least squares problems by iteratively minimizing the sum of squared residuals between the observed and predicted values. LM adds a damping parameter to the diagonal of the normal equations, which control.. Zettelkasten/Terminology Information 2023. 3. 5.

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  TSA (Tunicate Swarm Algorithm) Information The TSA (Tunicate Swarm Algorithm) is a bio-inspired metaheuristic optimization algorithm. Tunicates are bright bio-luminescent, cylindrical-shaped creatures found at depths of 500-800 m in the ocean with swarm behavior and jet propulsion being the main motivation behind TSA. TSA imitates the jet propulsion and swarm behaviors of tunicates during navigation and foraging to optimize n.. Zettelkasten/Terminology Information 2023. 3. 5.

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  ELM (Extreme Learning Machine) Information ELM (Extreme Learning Machine) is a type of feedforward neural network that was proposed as a fast and efficient alternative to traditional backpropagation-based neural networks. ELM can learn from large and complex datasets with high dimensional inputs and outputs, by randomly initializing the input-to-hidden weights and analytically computing the output weights using a least-square.. Zettelkasten/Terminology Information 2023. 3. 4.

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  BEMS (Building Energy Management System) Information BEMS (Building Energy Management System) is a system that manages the energy consumption and related operations of a building to optimize energy efficiency, reduce costs, and improve occupant comfort. BEMS typically includes hardware and software components, such as sensors, controllers, communication networks, and energy management software, that monitor and control various building.. Zettelkasten/Terminology Information 2023. 3. 4.

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  ML (Machine Learning) Information Machine Learning (ML) is a branch of artificial intelligence (AI) that involves the development and application of algorithms that can learn from data and make predictions or decisions without being explicitly programmed. ML can be supervised, unsupervised, or semi-supervised, depending on the type of training data and the desired output. In supervised learning, the algorithm learns .. Zettelkasten/Terminology Information 2023. 3. 3.

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  Persistence Information Persistence model is the simplest form of time series forecasting. It involves using the last observed value of a time series as the prediction for the next time step. The method is based on the assumption that the future values of a time series will be the same as the most recent past value. The persistence model is also called the "naive method". The persistence model is easy to im.. Zettelkasten/Terminology Information 2023. 3. 3.

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  PCA (Principal Component Analysis) Information PCA (Principal Component Analysis) is a statistical technique used to reduce the dimensionality of large datasets, while retaining as much of the variance or information as possible. PCA works by transforming the original variables into a new set of orthogonal variables, called principal components, that explain the maximum variance of the data, with the first component explaining th.. Zettelkasten/Terminology Information 2023. 3. 2.

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  MLR (Multiple Linear Regression) Information MLR (Multiple Linear Regression) is a statistical technique that models the relationship between a dependent variable and multiple independent variables. It assumes that the relationship between the dependent variable and each independent variable is linear. The goal of MLR is to find a linear equation that best fits the observed data by minimizing the sum of squared errors between t.. Zettelkasten/Terminology Information 2023. 3. 2.

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  STLF (Short-Term Load Forecasting) Information STLF (Short-Term Load Forecasting) is a process of predicting the future electricity demand in the near future, typically from a few hours to a few days ahead, at a fine temporal and spatial resolution. STLF is essential for ensuring the reliable and economic operation of the power grid, by enabling the utilities and system operators to plan and dispatch the generation, transmission,.. Zettelkasten/Terminology Information 2023. 3. 1.

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  ES (Exponential Smoothing) Information ES (Exponential Smoothing) is a statistical method that is used to estimate and forecast future values of a time series based on its past values. ES is a simple and popular method that is widely used in various fields, including business, finance, and economics. ES is a method that uses a weighted average of past observations to predict the future values of the time series. ES works .. Zettelkasten/Terminology Information 2023. 3. 1.

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  Friedman test Information The Friedman test is a non-parametric statistical test that compares three or more related samples to test if their underlying population distributions are different. The Friedman test is used when the data are not normally distributed or the assumptions of the parametric tests, such as the repeated measures ANOVA, are violated. The Friedman test ranks the observations in each sample.. Zettelkasten/Terminology Information 2023. 2. 28.

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  Xavier initialization Information Xavier initialization, also known as Glorot initialization, is a weight initialization technique used in neural networks. The main goal of Xavier initialization is to set the initial weights of the network in such a way that the variance of the outputs of each layer is approximately equal to the variance of its inputs. The initialization is performed by randomly initializing the weig.. Zettelkasten/Terminology Information 2023. 2. 28.

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  Wilcoxon test Information The Wilcoxon test, also known as the Wilcoxon rank-sum test or Mann-Whitney U test, is a non-parametric statistical test that compares two independent samples to test if their underlying population distributions are different. The Wilcoxon test is used when the data are not normally distributed or the assumptions of the parametric tests, such as the t-test, are violated. The Wilcoxon.. Zettelkasten/Terminology Information 2023. 2. 27.

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  ELU (Exponential Linear Unit) Information The ELU (Exponential Linear Unit) is a type of activation function commonly used in artificial neural networks. It is similar to the ReLU activation function, but with some modifications to handle negative inputs better. The ELU function is defined as f(x) = x for x ≥ 0 and f(x) = α(e^x - 1) for x < 0, where α is a hyperparameter that controls the output value for negative inputs. Th.. Zettelkasten/Terminology Information 2023. 2. 27.

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  MAPE (Mean Absolute Percentage Error) Information MAPE (Mean Absolute Percentage Error) is a measure of the accuracy of a forecasting model, expressed as a percentage of the actual values. MAPE is defined as the average of the absolute percentage errors (APEs) over the forecast horizon, multiplied by 100% to express it as a percentage: MAPE = (1/n) * sum(|(actual - forecast)/actual|) * 100% APE measures the size of the errors in the.. Zettelkasten/Terminology Information 2023. 2. 26.

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  PReLU (Parametric Rectified Linear Unit) Information PReLU (Parametric Rectified Linear Unit) is a variation of the ReLU activation function used in neural networks. It is called "parametric" because it has a learnable parameter that can be adjusted during the training process, unlike the standard ReLU function. The PReLU function is defined as f(x) = alpha * x for x = 0, where alpha is a learnable parameter. Th.. Zettelkasten/Terminology Information 2023. 2. 26.

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  CVRMSE (Coefficient of Variation of the Root Mean Squared Error) Information CVRMSE (Coefficient of Variation of the Root Mean Square Error) is a measure of the variation of the errors in a regression model, normalized by the mean of the target variable. CVRMSE is defined as the ratio of the root mean square error (RMSE) to the mean of the target variable, multiplied by 100% to express it as a percentage: CVRMSE = (RMSE / mean(target)) * 100% RMSE measures th.. Zettelkasten/Terminology Information 2023. 2. 25.

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  LReLU (Leaky Rectified Linear Unit) Information LReLU (Leaky Rectified Linear Unit) is a type of activation function used in deep learning models, particularly in convolutional neural networks (CNNs). It is similar to the ReLU (Rectified Linear Unit) activation function, but it allows for a small, non-zero gradient when the input is negative. The LReLU function is defined as f(x) = max(ax, x), where a is a small constant that is u.. Zettelkasten/Terminology Information 2023. 2. 25.

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  SELU (Scaled Exponential Linear Unit) Information SELU (Scaled Exponential Linear Unit) is an activation function for neural networks that was introduced in 2017 by Klambauer et al. SELU is a self-normalizing activation function, which means that it preserves the mean and variance of the activations across the layers, and thus reduces the vanishing/exploding gradients problem. SELU is defined as a piecewise function that is similar .. Zettelkasten/Terminology Information 2023. 2. 24.