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  Domain adaptation for time series forecasting via attention sharing Authors Xiaoyong Jin, Youngsuk Park, Danielle Maddix, Hao Wang, Yuyang Wang Title Domain adaptation for time series forecasting via attention sharing Publication Proceedings of the 39th International Conference on Machine Learning (ICML 2022) Volume 162 Issue x Pages 10280-10297 Year 2022 DOI x Introduction Background Time series forecasting has recently benefited from the development of DNN (De.. Zettelkasten/Paper Summarization 2023. 2. 21.

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  RF (Random Forest) Information RF (Random Forest) is a machine learning algorithm used for supervised learning problems, such as classification and regression. RF is an ensemble learning method that combines multiple decision trees to improve prediction accuracy. RF builds multiple decision trees using random subsets of the features and data, in order to reduce overfitting and improve generalization. RF uses a com.. Zettelkasten/Terminology Information 2023. 2. 20.

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  Short-term residential load forecasting: Impact of calendar effects and forecast granularity Authors Peter Lusis, Kaveh Rajab Khalilpour, Lachlan Andrew, Ariel Liebman Title Short-term residential load forecasting: Impact of calendar effects and forecast granularity Publication Applied energy Volume 205 Issue x Pages 654-669 Year 2017 DOI https://doi.org/10.1016/j.apenergy.2017.07.114 Introduction Background Electricity demand depends on weather, time, and socio-economic constraints. Lo.. Zettelkasten/Paper Summarization 2023. 2. 20.

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  DWT (Discrete Wavelet Transform) Information The DWT (Discrete Wavelet Transform) is a signal processing technique used for time-frequency analysis of signals. The DWT decomposes a signal into a set of wavelets, which are small waves with specific properties such as frequency and time localization. The DWT has advantages over other signal processing techniques, such as Fourier analysis, because it can analyze non-stationary sig.. Zettelkasten/Terminology Information 2023. 2. 20.

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  XGBoost (eXtreme Gradient Boosting) Information XGBoost (eXtreme Gradient Boosting) is a machine learning algorithm used for supervised learning problems, such as classification and regression. XGBoost is an ensemble learning method that combines multiple decision trees to improve prediction accuracy. XGBoost uses gradient boosting to iteratively train decision trees in a way that minimizes the overall prediction error. XGBoost us.. Zettelkasten/Terminology Information 2023. 2. 19.

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  Short-term commercial load forecasting based on peak-valley features with the TSA-ELM model Authors Mengran Zhou, Ziwei Zhu, Feng Hu, Kai Bian, Wenhao Lai, Tianyu Hu Title Short-term commercial load forecasting based on peak‐valley features with the TSA‐ELM model Publication Energy Science & Engineering Volume 10 Issue 8 Pages 2622-2636 Year 2022 DOI https://doi.org/10.1002/ese3.1203 Introduction Background The demand for electricity in buildings is increasing year after year. Building.. Zettelkasten/Paper Summarization 2023. 2. 19.

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  DFT (Discrete Fourier Transform) Information DFT (Discrete Fourier Transform) is a mathematical technique used for frequency analysis of a finite set of discrete data. DFT is a transformation of a discrete signal from the time domain to the frequency domain. It decomposes a signal into a sum of sinusoids of different frequencies. DFT is widely used in digital signal processing, image processing, and data compression. DFT is com.. Zettelkasten/Terminology Information 2023. 2. 19.

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  CCHP (Combined, cooling, heating and power) Information CCHP (Combined cooling, heating, and power) is an integrated energy system that produces electricity, heating, and cooling simultaneously from a single fuel source. CCHP systems can be powered by various fuel sources, including natural gas, biogas, and biomass. CCHP systems are highly efficient, with overall energy efficiency rates exceeding 80%, compared to around 50% for traditiona.. Zettelkasten/Terminology Information 2023. 2. 18.

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  A comparative analysis of artificial neural network architectures for building energy consumption forecasting Authors Jihoon Moon, Sungwoo Park, Seungmin Rho, Eenjun Hwang Title A comparative analysis of artificial neural network architectures for building energy consumption forecasting Publication International Journal of Distributed Sensor Networks Volume 15 Issue 9 Pages x Year 2019 DOI https://doi.org/10.1177/1550147719877616 Introduction Background Smart grids are a solution for energy shortage and.. Zettelkasten/Paper Summarization 2023. 2. 18.

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  LR (Linear Regression) Information LR (Linear Regression) is a statistical technique used to model the relationship between one or more independent variables and a dependent variable. LR assumes a linear relationship between the independent and dependent variables, meaning that the change in the dependent variable is proportional to the change in the independent variable. LR aims to find the line of best fit that mini.. Zettelkasten/Terminology Information 2023. 2. 17.

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  FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting Authors Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, Rong Jin Title FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting Publication Proceedings of the 39th International Conference on Machine Learning (ICML 2022) Volume 162 Issue x Pages 27268-27286 Year 2022 DOI x Introduction Background Long-term time series forecasting is a long-standing challenge.. Zettelkasten/Paper Summarization 2023. 2. 17.

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  Load forecasting Information Load forecasting is the process of estimating future electricity demand based on historical and current data, as well as other relevant factors such as weather patterns and economic trends. Load forecasting is essential for utilities and energy providers to plan for the future and ensure a reliable and efficient electricity supply. Load forecasting can be short-term (up to 24 hours),.. Zettelkasten/Terminology Information 2023. 2. 16.

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  Household electricity demand forecast based on context information and user daily schedule analysis from meter data Authors Yu-Hsiang Hsiao Title Household electricity demand forecast based on context information and user daily schedule analysis from meter data Publication IEEE Transactions on Industrial Informatics Volume 11 Issue 1 Pages 33-43 Year 2014 DOI https://doi.org/10.1109/TII.2014.2363584 Introduction Background Efficient energy usage is an important issue due to the limited energy sources and the .. Zettelkasten/Paper Summarization 2023. 2. 16.

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  Smart grid Information The smart grid is a modernized electrical grid that uses advanced digital communication and control technologies to improve the efficiency, reliability, and sustainability of the electricity system. It enables two-way communication between power generation sources, distribution networks, and end-users in real-time, allowing for greater coordination and control over the flow of electr.. Zettelkasten/Terminology Information 2023. 2. 15.

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  A Novel Short Receptive Field based Dilated Causal Convolutional Network Integrated with Bidirectional LSTM for Short-Term Load Forecasting Authors Umar Javed, Khalid Ijaz, Muhammad Jawad, Ikramullah Khosa, Ejaz Ahmad Ansari, Khurram Shabih Zaidi, Muhammad Nadeem Rafiq, and Noman Shabbir Title A Novel Short Receptive Field based Dilated Causal Convolutional Network Integrated with Bidirectional LSTM for Short-Term Load Forecasting Publication Expert Systems with Applications Volume 205 Issue x Pages x Year 2022 DOI https://doi.org/1.. Zettelkasten/Paper Summarization 2023. 2. 15.

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  A two-stage industrial load forecasting scheme for day-ahead combined cooling, heating and power scheduling Authors Sungwoo Park, Jihoon Moon, Seungwon Jung, Seungmin Rho, Sung Wook Baik, Eenjun Hwang Title A two-stage industrial load forecasting scheme for day-ahead combined cooling, heating and power scheduling Publication Energies Volume 13 Issue 2 Pages x Year 2020 DOI https://doi.org/10.3390/en13020443 Introduction Background There are growing concerns about environmental problems caused by carbo.. Zettelkasten/Paper Summarization 2023. 2. 14.