Journal ArticleDOI
A combined deep learning load forecasting model of single household resident user considering multi-time scale electricity consumption behavior
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TLDR
In this paper , a combined deep learning load forecasting model considering multi-time scale electricity consumption behavior of single household resident user to achieve high-accuracy and stable load forecasting is proposed.About:
This article is published in Applied Energy.The article was published on 2022-02-01. It has received 14 citations till now. The article focuses on the topics: Computer science & Electricity.read more
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Journal ArticleDOI
Transformer-Based Model for Electrical Load Forecasting
TL;DR: A transformer-based architecture for load forecasting is proposed by modifying the NLP transformer workflow, adding N-space transformation, and designing a novel technique for handling contextual features, which successfully handles time series with contextual data and outperforms the state-of-the-art Seq2Seq models.
Journal ArticleDOI
Machine learning based demand response scheme for IoT enabled PV integrated smart building
Balakumar P.,V. T.,C. K. +2 more
TL;DR: In this article , the authors proposed a day ahead dynamic pricing model for the demand response scheme, which minimizes the peak time demand and end-user electricity tariff by using a supervised machine learning approach.
Journal ArticleDOI
A Review of Data-Driven Building Energy Prediction
TL;DR: In this article , the authors reviewed 116 research papers on data-driven building energy prediction from the perspective of data and machine learning algorithms and discussed feasible techniques for prediction across time scales, building levels, and energy consumption types.
Journal ArticleDOI
Adaptive forecasting of diverse electrical and heating loads in community integrated energy system based on deep transfer learning
TL;DR: Considering the high randomness, obvious seasonality, and strong correlations between the multiple energy demands of community integrated energy system (CIES), the authors proposes an adaptive forecasting method for diverse loads of CIES based on deep transfer learning.
Journal ArticleDOI
Towards more accurate and explainable supervised learning-based prediction of deliverability for underground natural gas storage
TL;DR: In this paper , the authors applied Machine Learning (ML) techniques to predict the deliverability of underground natural gas storage (UNGS) in depleted reservoirs, where three baseline models were developed based on Support Vector Regression (SVR), Artificial Neural Network (ANN), and Random Forest (RF) algorithms.
References
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Journal Article
The mathematical theory of communication
Claude E. Shannon,Warren Weaver +1 more
TL;DR: The Mathematical Theory of Communication (MTOC) as discussed by the authors was originally published as a paper on communication theory more than fifty years ago and has since gone through four hardcover and sixteen paperback printings.
Journal ArticleDOI
Probabilistic electric load forecasting: A tutorial review
TL;DR: The need to invest in additional research, such as reproducible case studies, probabilistic load forecast evaluation and valuation, and a consideration of emerging technologies and energy policies in the probabilism load forecasting process are underlined.
Proceedings ArticleDOI
Building energy load forecasting using Deep Neural Networks
TL;DR: This paper presents a novel energy load forecasting methodology based on Deep Neural Networks, specifically, Long Short Term Memory (LSTM) algorithms that produced comparable results with the other deep learning methods for energy forecasting in literature.
Journal ArticleDOI
Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques
Cheng Fan,Fu Xiao,Shengwei Wang +2 more
TL;DR: This paper presents a data mining (DM) based approach to developing ensemble models for predicting next-day energy consumption and peak power demand, with the aim of improving the prediction accuracy.
Journal ArticleDOI
Robust optimization based optimal chiller loading under cooling demand uncertainty
TL;DR: This work proposes a robust optimization approach for uncertainty modeling of cooling demand in order to obtain robust chiller loading in the uncertain environment which cooling demand is supplied by multi-chiller system.