A Stacking Multi-Learning Ensemble Model for Predicting Near Real Time Energy Consumption Demand of Residential Buildings

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.

[1]  L. Mili,et al.  Electric Load Forecasting Based on Statistical Robust Methods , 2011, IEEE Transactions on Power Systems.

[2]  Muhd Zaimi Abd Majid,et al.  A global review of energy consumption, CO2 emissions and policy in the residential sector (with an overview of the top ten CO2 emitting countries) , 2015 .

[3]  Federico Divina,et al.  Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting , 2018 .

[4]  Wan He,et al.  Load Forecasting via Deep Neural Networks , 2017, ITQM.

[5]  Jianhua Zhang,et al.  A hybrid forecasting model with parameter optimization for short-term load forecasting of micro-grids , 2014 .

[6]  Lee-Ing Tong,et al.  Forecasting time series using a methodology based on autoregressive integrated moving average and genetic programming , 2011, Knowl. Based Syst..

[7]  Wei-Chiang Hong,et al.  Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artific , 2011 .

[8]  Chen Wang,et al.  A combined model based on multiple seasonal patterns and modified firefly algorithm for electrical load forecasting , 2016 .

[9]  Garrison W. Cottrell,et al.  A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction , 2017, IJCAI.

[10]  Hak-Keung Lam,et al.  A novel genetic-algorithm-based neural network for short-term load forecasting , 2003, IEEE Trans. Ind. Electron..

[11]  Lambros Ekonomou,et al.  Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models , 2008 .

[12]  R.-H. Liang,et al.  Fuzzy linear programming: an application to hydroelectric generation scheduling , 1994 .

[13]  Mohammed E. El-Telbany,et al.  Short-term forecasting of Jordanian electricity demand using particle swarm optimization , 2008 .

[14]  S. Muto,et al.  Regression based peak load forecasting using a transformation technique , 1994 .

[15]  Asifullah Khan,et al.  Intelligent and robust prediction of short term wind power using genetic programming based ensemble of neural networks , 2017 .

[16]  M. El-Hawary,et al.  Load forecasting via suboptimal seasonal autoregressive models and iteratively reweighted least squares estimation , 1993 .

[17]  Yuan Zhang,et al.  Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network , 2019, IEEE Transactions on Smart Grid.

[18]  Shyh-Jier Huang,et al.  Short-term load forecasting via ARMA model identification including non-Gaussian process considerations , 2003 .

[19]  Chen Jie,et al.  Wind speed forecasting using nonlinear-learning ensemble of deep learning time series prediction and extremal optimization , 2018, Energy Conversion and Management.

[20]  Saudi Arabia,et al.  SHORT-TERM PEAK DEMAND FORECASTING IN FAST DEVELOPING UTILITY WITH INHERIT DYNAMIC LOAD CHARACTERISTICS , 1990 .

[21]  Wil L. Kling,et al.  Support Vector Machine in Prediction of Building Energy Demand Using Pseudo Dynamic Approach , 2015, ArXiv.

[22]  Michael T. Manry,et al.  Comparison of very short-term load forecasting techniques , 1996 .

[23]  Ping-Feng Pai,et al.  Support Vector Machines with Simulated Annealing Algorithms in Electricity Load Forecasting , 2005 .

[24]  Yitao Liu,et al.  Deep learning based ensemble approach for probabilistic wind power forecasting , 2017 .

[25]  I. J. Ramirez-Rosado,et al.  Distribution planning of electric energy using fuzzy models , 1996 .

[26]  Daniel L. Marino,et al.  Building energy load forecasting using Deep Neural Networks , 2016, IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society.

[27]  M. Crawford,et al.  An Adaptive Nonlinear Predictor with Orthogonal Escalator Structure for Short-Term Load Forecasting , 1989, IEEE Power Engineering Review.

[28]  Sheng Wanxing,et al.  Short-term load forecasting using artificial immune network , 2002, Proceedings. International Conference on Power System Technology.

[29]  Saifur Rahman,et al.  Analysis and Evaluation of Five Short-Term Load Forecasting Techniques , 1989, IEEE Power Engineering Review.

[30]  Jack Kelly,et al.  The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes , 2014, Scientific Data.

[31]  Marimuthu Palaniswami,et al.  Improving load forecasting based on deep learning and K-shape clustering , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

[32]  Nashat T. AL-Jallad,et al.  Short-Term Forecasting for Energy Consumption through Stacking Heterogeneous Ensemble Learning Model , 2018, Energies.

[33]  Mauro Conti,et al.  Computational intelligence approaches for energy load forecasting in smart energy management grids: state of the art, future challenges, and research directionsand Research Directions , 2018 .

[34]  Sen Guo,et al.  A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm , 2013, Knowl. Based Syst..

[35]  Y.-Y. Hsu,et al.  Fuzzy expert systems: an application to short-term load forecasting , 1992 .

[36]  T. Hesterberg,et al.  A regression-based approach to short-term system load forecasting , 1989, Conference Papers Power Industry Computer Application Conference.

[37]  Yueren Wang,et al.  A novel ensemble learning approach to support building energy use prediction , 2018 .

[38]  W. M. Grady,et al.  Enhancement, implementation, and performance of an adaptive short-term load forecasting algorithm , 1991 .

[39]  Irena Koprinska,et al.  Combining pattern sequence similarity with neural networks for forecasting electricity demand time series , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

[40]  Lino Guzzella,et al.  SHORT-TERM THERMAL AND ELECTRIC LOAD FORECASTING IN BUILDINGS , 2013 .

[41]  Regina Lamedica,et al.  A neural network based technique for short-term forecasting of anomalous load periods , 1996 .

[42]  Haipeng Wang,et al.  An Ensemble Model Based on Machine Learning Methods for Short-term Power Load Forecasting , 2018, IOP Conference Series: Earth and Environmental Science.