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  PSO (Particle Swarm Optimization) Information PSO (Particle Swarm Optimization) is a metaheuristic optimization algorithm that is inspired by the social behavior of bird flocks or fish schools. The algorithm starts by randomly initializing a population of particles, each representing a potential solution to the optimization problem, and assigning them random velocities. The particles then move through the search space, guided by.. Zettelkasten/Terminology Information 2023. 3. 8.

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  EPO (Emperor Penguin Optimization) Information EPO (Emperor Penguin Optimization) is based on the observation that emperor penguins exhibit a group foraging behavior that helps them find food in the harsh Antarctic environment. EPO uses a population of solutions, called penguins, that move through the search space in search of the optimum solution. The penguins are divided into two groups: leaders and followers. Leaders explore t.. Zettelkasten/Terminology Information 2023. 3. 8.

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  FFNN (Feed Forward Neural Network) Information A FFNN (Feed-Forward Neural Network) is a type of artificial neural network in which the information flows only in one direction, from input layer to output layer. The network consists of one or more hidden layers, each of which contains a number of neurons or processing units that transform the input signals using a nonlinear activation function, such as the sigmoid, ReLU, or tanh f.. Zettelkasten/Terminology Information 2023. 3. 7.

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  MVO (Multi-Verse Optimization) Information MVO (Multi-Verse Optimization) is a swarm-based metaheuristic optimization algorithm used for solving optimization problems. It is inspired by the concept of multiple universes, where each universe represents a possible solution to the optimization problem. The algorithm is based on three main phases: Initialization, Evolution, and Selection. In the Initialization phase, the algorith.. Zettelkasten/Terminology Information 2023. 3. 7.

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  CMIFS (Conditional Mutual Information-based Feature Selection) Information CMIFS (Conditional Mutual Information-based Feature Selection) is a type of feature selection method that aims to identify the most informative subset of features that are relevant to a target variable. CMIFS ranks the features based on their conditional dependence with the target variable, given the other features, and selects the top-ranked features that have the highest conditiona.. Zettelkasten/Terminology Information 2023. 3. 6.

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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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  Efficient Residential Electric Load Forecasting viaTransfer Learning and Graph Neural Networks Authors Di Wu, Weixuan Lin Title Efficient Residential Electric Load Forecasting via Transfer Learning and Graph Neural Networks Publication IEEE Transactions on Smart Grid Volume x Issue x Pages x Year 2022 DOI https://doi.org/10.1109/TSG.2022.3208211 Introduction Background Electric load forecasting is crucial for the efficient operation of modern power grids. Short-term load forecasting (STLF.. Zettelkasten/Paper Summarization 2023. 3. 3.

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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.