Nonlinear autoregressive and random forest approaches to forecasting electricity load for utility energy management systems

Abstract The capability to forecast how differences in patterns of utilization in various kinds of loads can influence energy usage is an essential effort to decrease carbon emissions and demand-side energy management. The difference in weather change starts as the first step to change the energy consumption pattern in the domestic, commercial and industrial sector. To find the change in climate and their impact on energy usage, this study examines the medium-term (MT) and long-term (LT) energy prediction for utilities, independent power producers and industrial customers to estimate the energy usage requirement of large-scale city-wide by means of using the nonlinear autoregressive model (NARM), linear model using stepwise regression (LMSR) and random forest (least square boosting) (LSBoost) approaches, based on actual environmental as well as energy consumption data. The irregular load pattern recognition to remove the abnormal trend in actual energy usage is performed by applying the outlier detection and clustering analysis. The coefficient of variation (CV) of LSBoost model is 5.019%, 3.159%, 3.292% and 3.184% in summer, autumn, winter and spring season respectively. The machine learning (ML) techniques are validated and compared based on performance and accuracy with the previously existing Gaussian process regression (GPR) model. The optimal modeling of city-wide energy demand prediction using ML-based models are guaranteed the accurate operation and design of distributed energy systems.

[1]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[2]  Raj Bhatnagar,et al.  Generation of accurate weather files using a hybrid machine learning methodology for design and analysis of sustainable and resilient buildings , 2016 .

[3]  David A. Cartes,et al.  Application of artificial intelligence to stator winding fault diagnosis in Permanent Magnet Synchronous Machines , 2013 .

[4]  Xiaodong Cao,et al.  Building energy-consumption status worldwide and the state-of-the-art technologies for zero-energy buildings during the past decade , 2016 .

[5]  K. Chua,et al.  Forecasting long-term electricity demand for cooling of Singapore’s buildings incorporating an innovative air-conditioning technology , 2016 .

[6]  Shengwei Wang,et al.  Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques , 2014 .

[7]  B. Dong,et al.  Applying support vector machines to predict building energy consumption in tropical region , 2005 .

[8]  Tanveer Ahmad,et al.  Potential of three variant machine-learning models for forecasting district level medium-term and long-term energy demand in smart grid environment , 2018, Energy.

[9]  Mark O'Malley,et al.  Capacity value estimation of a load-shifting resource using a coupled building and power system model , 2017 .

[10]  Paul Grigas,et al.  A New Perspective on Boosting in Linear Regression via Subgradient Optimization and Relatives , 2015, ArXiv.

[11]  Isabel M. Horta,et al.  A scenario-based approach for assessing the energy performance of urban development pathways , 2018, Sustainable Cities and Society.

[12]  R. Tibshirani,et al.  REJOINDER TO "LEAST ANGLE REGRESSION" BY EFRON ET AL. , 2004, math/0406474.

[13]  Jan Adamowski,et al.  Coupling machine learning methods with wavelet transforms and the bootstrap and boosting ensemble approaches for drought prediction , 2016 .

[14]  Gang Liu,et al.  Development of a general sustainability indicator for renewable energy systems: A review , 2014 .

[15]  António E. Ruano,et al.  Prediction of building's temperature using neural networks models , 2006 .

[16]  Ali Mohammad Ranjbar,et al.  Demand side management for a residential customer in multi-energy systems , 2016 .

[17]  Jan G. Pieters,et al.  Modelling greenhouse temperature using system identification by means of neural networks , 2004, Neurocomputing.

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

[19]  Fu Xiao,et al.  Unsupervised data analytics in mining big building operational data for energy efficiency enhancement: A review , 2018 .

[20]  C. Senabre,et al.  Application of SOM neural networks to short-term load forecasting: The Spanish electricity market case study , 2012 .

[21]  Jie Zhu,et al.  Review of passive solar heating and cooling technologies , 2010 .

[22]  J. Friedman.  Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .

[23]  Gang Liu,et al.  Modeling of district load forecasting for distributed energy system , 2017 .

[24]  Henrik Madsen,et al.  Identification of the main thermal characteristics of building components using MATLAB , 2008 .

[25]  Agata Filipowska,et al.  The comparison of medium-term energy demand forecasting methods for the need of microgrid management , 2014, 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[26]  Davy Geysen,et al.  Operational thermal load forecasting in district heating networks using machine learning and expert advice , 2017, ArXiv.

[27]  G. Mustafaraj,et al.  Development of room temperature and relative humidity linear parametric models for an open office using BMS data , 2010 .

[28]  Lynne E. Parker,et al.  Energy and Buildings , 2012 .

[29]  Dominic T. J. O'Sullivan,et al.  The suitability of machine learning to minimise uncertainty in the measurement and verification of energy savings , 2018 .

[30]  Jiejin Cai,et al.  Applying support vector machine to predict hourly cooling load in the building , 2009 .

[31]  S. C. Kaushik,et al.  State-of-the-art of solar thermal power plants—A review , 2013 .

[32]  Tanveer Ahmad,et al.  Utility companies strategy for short-term energy demand forecasting using machine learning based models , 2018 .

[33]  Young Tae Chae,et al.  Building energy performance evaluation of building integrated photovoltaic (BIPV) window with semi-transparent solar cells , 2014 .

[34]  Maria del Carmen Pegalajar Jiménez,et al.  An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings , 2016 .

[35]  Per Fahlén,et al.  Estimation of operative temperature in buildings using artificial neural networks , 2006 .

[36]  D. Gyalistras,et al.  Climate warming impact on degree-days and building energy demand in Switzerland , 2006 .

[37]  Jonathan A. Wright,et al.  A comparison of approaches to stepwise regression on variables sensitivities in building simulation and analysis , 2016 .

[38]  Tanveer Ahmad,et al.  Supervised based machine learning models for short, medium and long-term energy prediction in distinct building environment , 2018, Energy.

[39]  Yacine Rezgui,et al.  Electrical load forecasting models: A critical systematic review , 2017 .

[40]  Tanveer Ahmad,et al.  Short and medium-term forecasting of cooling and heating load demand in building environment with data-mining based approaches , 2018 .

[41]  Alicja Kolasa-Wiecek,et al.  Stepwise multiple regression method of greenhouse gas emission modeling in the energy sector in Poland. , 2015, Journal of environmental sciences.

[42]  Tanveer Ahmad,et al.  A comprehensive overview on the data driven and large scale based approaches for forecasting of building energy demand: A review , 2018 .

[43]  Elias K. Stefanakos,et al.  Thermal energy storage technologies and systems for concentrating solar power plants , 2013 .

[44]  Jie Chen,et al.  Prediction of room temperature and relative humidity by autoregressive linear and nonlinear neural n , 2011 .