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  NYISO (New York Independent System Operator) dataset Information The NYISO (New York Independent System Operator) dataset is a publicly available dataset of energy measurements and market data from the NYISO region, intended for use in research on energy markets, grid operations, and related topics. The dataset is managed by NYISO, the organization responsible for operating the bulk power system and administering wholesale electricity markets in t.. Zettelkasten/Dataset Information 2023. 3. 29.

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  Statistical model Information A statistical model is a mathematical framework used to describe the relationship between a set of variables. It allows us to make inferences about the underlying population by analyzing a sample of data. Statistical models can be used for a wide range of applications, including forecasting, classification, and regression. There are two main types of statistical models: parametric an.. Zettelkasten/Terminology Information 2023. 3. 29.

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  AEMO (Australian Energy Market Operator) dataset Information The AEMO (Australian Energy Market Operator) dataset is a publicly available dataset of energy measurements and market data from the National Electricity Market (NEM) in Australia, intended for use in research on energy markets, grid operations, and related topics. The dataset is managed by AEMO, the organization responsible for operating the NEM and administering wholesale electrici.. Zettelkasten/Dataset Information 2023. 3. 28.

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  ERCOT (Electric Reliability Council of Texas) Information The ERCOT (Electric Reliability Council of Texas) dataset is a publicly available dataset of energy measurements and market data from the ERCOT region, intended for use in research on energy markets, grid operations, and related topics. The dataset is managed by ERCOT, the organization responsible for operating the bulk power system and administering wholesale electricity markets in .. Zettelkasten/Dataset Information 2023. 3. 27.

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  LTLF (Long-Term Load Forecasting) Information LTLF (Long-Term Load Forecasting) is a type of load forecasting that predicts the energy demand for a period of several years, typically ranging from 5 to 20 years into the future. LTLF models are typically used by utility companies, energy planners, and policy makers to make long-term investment decisions, plan for future capacity requirements, and ensure energy security and reliabi.. Zettelkasten/Terminology Information 2023. 3. 27.

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  MTLF (Mid-Term Load Forecasting) Information MTLF (Mid-Term Load Forecasting) is a type of load forecasting used to predict energy consumption and demand over an intermediate period of time, typically ranging from a few days to several weeks. MTLF is generally used by power utilities and energy companies to better plan their energy generation, transmission, and distribution activities, optimize energy production, and improve gr.. Zettelkasten/Terminology Information 2023. 3. 27.

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  ISO-NE (ISO New England) dataset Information The ISO-NE (ISO New England) dataset is a publicly available dataset of real-time energy measurements and market data from the ISO New England region, intended for use in research on energy markets, grid operations, and related topics. The dataset is managed by ISO New England, the organization responsible for operating the bulk power system and administering wholesale electricity ma.. Zettelkasten/Dataset Information 2023. 3. 26.

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  VSTLF (Very Short-Term Load Forecasting) Information Very Short-Term Load Forecasting (VSTLF) is a type of load forecasting that predicts the power consumption of an electrical grid for a few minutes up to a few hours ahead of time, typically up to 24 hours in advance. The forecasting is based on the assumption that the load is influenced by factors such as weather, time of day, day of the week, and special events. VSTLF plays a vital .. Zettelkasten/Terminology Information 2023. 3. 26.

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  Min-Max scaling Information Min-Max scaling is a technique used to scale numerical features to a fixed range of [0, 1]. It is a linear scaling method that linearly transforms each feature to the specified range. The formula used for Min-Max scaling is: X_scaled = (X - X_min) / (X_max - X_min) where X_scaled is the scaled feature value, X is the original feature value, X_min is the minimum value of the feature, .. Zettelkasten/Terminology Information 2023. 3. 25.

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  Pecan street dataset Information The Pecan Street dataset is a publicly available dataset of high-frequency energy and environmental data from hundreds of homes, intended for use in research on energy efficiency, renewable energy integration, and related topics. The dataset was created in 2009 by Pecan Street Inc., a research and development organization focused on smart grid technologies and sustainable energy solu.. Zettelkasten/Dataset Information 2023. 3. 25.

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  Adam (Adaptive Moment Estimation) Information Adam (Adaptive Moment Estimation) is a stochastic gradient descent optimization algorithm commonly used for training deep neural networks. It is an adaptive learning rate optimization algorithm that combines the advantages of both AdaGrad and RMSProp optimizers. The algorithm maintains an exponentially decaying average of past gradients and past squared gradients to compute the adapt.. Zettelkasten/Terminology Information 2023. 3. 24.

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  UK-DALE (UK Domestic Appliance-Level Electricity) dataset Information The UK-DALE (UK Domestic Appliance-Level Electricity) dataset is a publicly available dataset of high-frequency power measurements from multiple homes, intended for use in research on energy disaggregation and related topics. The dataset was created in 2013 by researchers at the University of Oxford, and is hosted by the Energy Group at the university. The UK-DALE dataset contains da.. Zettelkasten/Dataset Information 2023. 3. 24.

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  ANN (Artificial Neural Network) Information An ANN (Artificial Neural Network) is a machine learning model that is inspired by the structure and function of the human brain and nervous system. ANN consists of interconnected processing nodes (neurons) that are arranged in layers (input, hidden, and output) to process information and perform a variety of tasks, such as classification, regression, and prediction. Each neuron in a.. Zettelkasten/Terminology Information 2023. 3. 23.

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  REDD (Reference Energy Disaggregation Data) dataset Information The REDD (Reference Energy Disaggregation Data) dataset is a publicly available dataset of high-frequency power measurements from multiple homes, intended for use in research on energy disaggregation and related topics. The dataset was created in 2011 by the NILM (non-intrusive load monitoring) community, and is hosted by the Electric Power Research Institute (EPRI). The REDD dataset.. Zettelkasten/Dataset Information 2023. 3. 23.

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  KNN (K-Nearest Neighbors) Information KNN (K-Nearest Neighbors) is a type of machine learning algorithm used for classification and regression. It's a non-parametric algorithm, which means it doesn't make any assumptions about the underlying data distribution. The algorithm works by finding the K number of nearest data points to a given data point based on a similarity metric, usually Euclidean distance. The value of K i.. Zettelkasten/Terminology Information 2023. 3. 22.

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  GEFCom (Global Energy Forecasting Competition) dataset Information The GEFCom (Global Energy Forecasting Competition) dataset is a collection of time series data used for benchmarking and evaluating forecasting models in the energy industry. GEFCom was launched in 2012 by the IEEE Power and Energy Society's Working Group on Energy Forecasting and has since become a widely recognized benchmark for energy forecasting. The dataset includes historical e.. Zettelkasten/Dataset Information 2023. 3. 22.

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  ARX (AutoRegressive with eXogenous inputs) Information ARX (AutoRegressive with eXogenous inputs) is a linear model used in time-series analysis and forecasting, where the output variable (Y) is modeled as a function of its own lagged values (AR term) and the lagged values of one or more exogenous input variables (X term). The ARX model is a combination of an AR model and an external input, making it a more general class of models compar.. Zettelkasten/Terminology Information 2023. 3. 21.

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  OE (Output Error) Information The OE (Output Error) model is a type of linear dynamic model that is commonly used in system identification and control theory. The model is based on the idea that the output of a system is a function of its input and past outputs, as well as any external disturbances that may be present. The model is expressed in terms of a transfer function that relates the input to the output, as.. Zettelkasten/Terminology Information 2023. 3. 20.

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  Cold-start problem Information The cold-start problem is a challenge in recommendation systems where there is not enough data about a new user or item to make accurate predictions. It arises when there are no historical interactions or preferences available for a user or item, or when the available data is sparse or unreliable. The cold-start problem can occur for new users who have just joined the platform or for.. Zettelkasten/Terminology Information 2023. 3. 20.

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  ARMAX (AutoRegressive Moving Average model with eXogenous inputs) Information ARMAX (AutoRegressive Moving Average model with eXogenous inputs) is a type of statistical model that combines autoregressive (AR) and moving average (MA) models with external variables, also known as exogenous variables. The model assumes that the output variable depends on its past values, the past values of the error term, and past values of the exogenous variables. ARMAX models a.. Zettelkasten/Terminology Information 2023. 3. 19.