Proceedings ArticleDOI
A Stacking Multi-Learning Ensemble Model for Predicting Near Real Time Energy Consumption Demand of Residential Buildings
Andreea Valeria Ves,Nicoleta Ghitescu,Claudia Pop,Marcel Antal,Tudor Cioara,Ionut Anghel,Ioan Salomie +6 more
- pp 183-189
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TLDR
A stacked multi-learning ensemble model is proposed combining Gradient Boosting Regression, Multi-Layer Neural Networks and Long Short Term Memory Networks followed by a Linear Regressor for forecasting residential energy demands both at individual and aggregated levels.Abstract:
The aim of this paper is to present a novel energy consumption forecasting solution for predicting energy demand at the level of residential buildings based on their historical consumption profile for a seamless integration with the session-based Energy Markets developed within Smart Energy Grids that integrate renewable energy sources. To overcome the drawbacks and lack of accuracy of existing prediction models, a stacked multi-learning ensemble model is proposed combining Gradient Boosting Regression, Multi-Layer Neural Networks and Long Short Term Memory Networks followed by a Linear Regressor for forecasting residential energy demands both at individual and aggregated levels. The proposed ensemble predictor is evaluated using the open-access UK-DALE dataset containing historical energy traces for 5 households spreading over several years, obtaining a best MAPE of 1.59%, a RMSE of 6. 19 kWh and a MAE of 5. 60 kWh on the aggregated dataset, proving the high accuracy and stability of the proposed solution as well as the feasibility of using ensemble models for residential building energy demand forecast for integration with session-based energy marketplaces.read more
Citations
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Journal ArticleDOI
Electric vehicle energy consumption prediction using stacked generalization: an ensemble learning approach
TL;DR: ESG is a weighted combination of multiple base regression models to one meta-regressor, which enhances the model prediction and decreases the model variance over a single regressor model, to predict the EVs’ energy consumption.
Journal ArticleDOI
A Review of Deep Learning Techniques for Forecasting Energy Use in Buildings
Jason Runge,Radu Zmeureanu +1 more
TL;DR: In this paper, the authors provide an extensive review of deep learning-based techniques applied to forecasting the energy use in buildings to explore its effectiveness and application potential and present a breakdown of current trends identified in published research along with a discussion of how deep learningbased models have been applied for feature extraction and forecasting.
Journal ArticleDOI
A Scoping Review of Deep Neural Networks for Electric Load Forecasting
TL;DR: In this article, a thorough literature search is conducted, which outlines and analyses essential aspects regarding deep learning load forecasts in the energy domain, and recommends using a hybrid deep learning multivariate model consisting of a convolutional and recurrent neural network based on the scoping review.
Journal ArticleDOI
A Tri-Layer Optimization Framework for Day-Ahead Energy Scheduling Based on Cost and Discomfort Minimization
Paraskevas Koukaras,Paschalis A. Gkaidatzis,Napoleon Bezas,Tommaso Bragatto,Federico Carere,Francesca Santori,Marcel Antal,Dimosthenis Ioannidis,Christos Tjortjis,Dimitrios Tzovaras +9 more
TL;DR: A framework for a multi-objective analysis, acting as a novel tool that offers responses for optimal energy management through a decision support system, whilst abiding with real-world system constraints is proposed.
Proceedings ArticleDOI
Internet of Energy: Ensemble Learning through Multilevel Stacking for Load Forecasting
TL;DR: This work presents a novel ensemble learning mechanism through multi-level stacking for the load forecasting of electricity load forecasting that uses computational intelligence techniques such as Random Forest, Cubist, k-Nearest Neighbors, and eXtreme Gradient Boosting to train participating prediction models at various levels of stacking.
References
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Journal ArticleDOI
Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network
TL;DR: The proposed LSTM approach outperforms the other listed rival algorithms in the task of short-term load forecasting for individual residential households and is comprehensively compared to various benchmarks including the state-of-the-arts in the field of load forecasting.
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A global review of energy consumption, CO2 emissions and policy in the residential sector (with an overview of the top ten CO2 emitting countries)
TL;DR: In this article, the status and current trends of energy consumption, CO2 emissions and energy policies in the residential sector, both globally and in those ten countries, were reviewed, and it was found that global residential energy consumption grew by 14% from 2000 to 2011, where population, urbanization and economic growth have been the main driving factors.
Journal ArticleDOI
A regression-based approach to short-term system load forecasting
TL;DR: In this paper, a linear regression-based model for the calculation of short-term system load forecasts is described, and the model's most significant new aspects fall into the following areas: accurate holiday modeling by using binary variables, temperature modelling by using heating and cooling degree functions; robust parameter estimation and parameter estimation under heteroskedasticity by using weighted least-squares linear regression techniques; the use of reverse errors-in-variables' techniques to mitigate the effects on load forecasts of potential errors in the explanatory variables; and distinction between time-independent daily peak load forecasts
Proceedings ArticleDOI
A dual-stage attention-based recurrent neural network for time series prediction
TL;DR: Zhang et al. as discussed by the authors proposed a dual-stage attention-based recurrent neural network (DA-RNN) to capture long-term temporal dependencies appropriately and select the relevant driving series to make predictions.
Analysis andEvaluation ofFiveShort-Term Load Forecasting Techniques
Ibrahim Moghram,Saifur Rahman +1 more
TL;DR: A comparative evaluation of five short-term load forecasting techniques is presented and the transfer function (TF) approach gave the best result, whereas for the peak winter day the TF approach resulted in the next to the worst accuracy.
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