Demand forecast of PV integrated bioclimatic buildings using ensemble framework

Abstract Buildings are one of the major sources of electricity and greenhouse gas emission (GHG) in urban areas all around the world. Since a large integration of solar energy is observed in the form of rooftop photovoltaic (PV) units, electricity use of buildings is highly uncertain due to intermittent nature of solar output power. This leads to poor energy management for both network operators and building owners. In addition, uncertain metrological conditions, diversity and complexity of buildings are big hurdles to accurate prediction of the demand. To improve accuracy of load demand forecast of PV integrated smart building, a hybrid ensemble framework is proposed in this paper. This is based on a combination of five different predictors named as backpropagation neural network (BPNN), Elman neural network (EN), Autoregressive Integrated Moving Average (ARIMA), feed forward neural network (FNN), radial basis function (RBF) and their wavelet transform (WT) models. WT is applied to historical load data to remove the spikes and fluctuations. FNN and RBF network were trained with particle swarm optimization (PSO) for higher forecast accuracy. The output of each predictor in the ensemble network is combined using Bayesian model averaging (BMA). The proposed framework is tested using real data of two practical PV integrated smart buildings in a big university environment. The results indicate that the proposed framework show improvement in average forecast normalized root mean square error (nRMSE) around 17% and 20% in seasonal daily and seasonal weekly case studies, respectively. In addition, proposed framework also produces lowest of nRMSE about 3.88% in seasonal monthly forecast of smart buildings with rooftop PV as compared to benchmark model. The proposed forecast framework provides consistent forecast results for global change institute (GCI) and advance engineering building (AEB) during seasonal daily and weekly comparison.

[1]  Gong Li,et al.  Application of Bayesian model averaging in modeling long-term wind speed distributions , 2010 .

[2]  Siti Zaiton Mohd Hashim,et al.  Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm , 2012, Appl. Math. Comput..

[3]  Yu Xue,et al.  Prediction of particulate matter at street level using artificial neural networks coupling with chaotic particle swarm optimization algorithm , 2014 .

[4]  Paras Mandal,et al.  A Hybrid Intelligent Model for Deterministic and Quantile Regression Approach for Probabilistic Wind Power Forecasting , 2014, IEEE Transactions on Power Systems.

[5]  Jan R. Magnus,et al.  A comparison of two model averaging techniques with an application to growth empirics , 2010 .

[6]  Ming Qi,et al.  Application of Chaos theory in Cascaded Five Levels Variable Frequency and Variable Speed System , 2010, 2010 Asia-Pacific Power and Energy Engineering Conference.

[7]  Akin Tascikaraoglu,et al.  A demand side management strategy based on forecasting of residential renewable sources: A smart home system in Turkey , 2014 .

[8]  Gao,et al.  Bagging Neural Networks for Predicting Water Consumption , 2005 .

[9]  Jing Shi,et al.  On comparing three artificial neural networks for wind speed forecasting , 2010 .

[10]  Marwan M. Mahmoud,et al.  Assessment of Artificial Neural Networks for Hourly Solar Radiation Prediction , 2012 .

[11]  A. Raftery,et al.  Using Bayesian Model Averaging to Calibrate Forecast Ensembles , 2005 .

[12]  Tao Lu,et al.  Modeling and forecasting energy consumption for heterogeneous buildings using a physical -statistical approach , 2015 .

[13]  Alireza Khotanzad,et al.  A Neuro-Fuzzy Approach to Short-Term Load Forecasting in a Price-Sensitive Environment , 2002, IEEE Power Engineering Review.

[14]  Teresa Wu,et al.  Short-term building energy model recommendation system: A meta-learning approach , 2016 .

[15]  J. M. Sloughter,et al.  Probabilistic Wind Speed Forecasting Using Ensembles and Bayesian Model Averaging , 2010 .

[16]  Abbas Khosravi,et al.  A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings , 2015 .

[17]  Abbas Khosravi,et al.  Examining performance of aggregation algorithms for neural network-based electricity demand forecasting , 2015 .

[18]  D. H. Vu,et al.  A variance inflation factor and backward elimination based robust regression model for forecasting monthly electricity demand using climatic variables , 2015 .

[19]  M. Raza,et al.  On recent advances in PV output power forecast , 2016 .

[20]  Kevin M. Smith,et al.  Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy , 2014 .

[21]  Mikko Kolehmainen,et al.  Intelligent analysis of energy consumption in school buildings , 2016 .

[22]  Krzysztof Siwek,et al.  Improving the accuracy of prediction of PM10 pollution by the wavelet transformation and an ensemble of neural predictors , 2012, Eng. Appl. Artif. Intell..

[23]  Abbas Khosravi,et al.  Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[24]  Jing Shi,et al.  Bayesian adaptive combination of short-term wind speed forecasts from neural network models , 2011 .

[25]  Chirag Deb,et al.  Forecasting diurnal cooling energy load for institutional buildings using Artificial Neural Networks , 2016 .

[26]  Vittorio Cesarotti,et al.  Energy consumption control automation using Artificial Neural Networks and adaptive algorithms: Proposal of a new methodology and case study , 2016 .

[27]  Er-Wei Bai,et al.  Developing a whole building cooling energy forecasting model for on-line operation optimization using proactive system identification , 2016 .

[28]  Song Li,et al.  Short-term load forecasting by wavelet transform and evolutionary extreme learning machine , 2015 .

[29]  David R. Anderson,et al.  Model selection and multimodel inference : a practical information-theoretic approach , 2003 .

[30]  Giuseppe De Luca,et al.  Bayesian Model Averaging and Weighted-Average Least Squares: Equivariance, Stability, and Numerical Issues , 2011 .

[31]  Yan Su,et al.  Forecasting the daily power output of a grid-connected photovoltaic system based on multivariate adaptive regression splines , 2016 .

[32]  Wasserman,et al.  Bayesian Model Selection and Model Averaging. , 2000, Journal of mathematical psychology.

[33]  V M F Mendes,et al.  Hybrid Wavelet-PSO-ANFIS Approach for Short-Term Electricity Prices Forecasting , 2011, IEEE Transactions on Power Systems.

[34]  Federico Silvestro,et al.  Electrical consumption forecasting in hospital facilities: An application case , 2015 .

[35]  Jui-Sheng Chou,et al.  Time series analytics using sliding window metaheuristic optimization-based machine learning system for identifying building energy consumption patterns , 2016 .

[36]  Zuhairi Baharudin,et al.  A Comparative Analysis of Neural Network Based Short Term Load Forecast Models for Anomalous Days Load Prediction , 2014, J. Comput..

[37]  Manuel R. Arahal,et al.  A prediction model based on neural networks for the energy consumption of a bioclimatic building , 2014 .

[38]  Mithulananthan Nadarajah,et al.  An improved WT and NN ensemble demand forecast model for PV integrated smart buildings , 2016, 2016 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia).

[39]  S. Fan,et al.  Short-term load forecasting based on an adaptive hybrid method , 2006, IEEE Transactions on Power Systems.

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

[41]  Paras Mandal,et al.  A novel hybrid approach using wavelet, firefly algorithm, and fuzzy ARTMAP for day-ahead electricity price forecasting , 2013, IEEE Transactions on Power Systems.

[42]  Luis C. Dias,et al.  Multi-objective optimization for building retrofit: A model using genetic algorithm and artificial neural network and an application , 2014 .

[43]  Manoj Tripathy,et al.  Short-term load/price forecasting in deregulated electric environment using ELMAN neural network , 2015, 2015 International Conference on Energy Economics and Environment (ICEEE).

[44]  S. Sorooshian,et al.  Multi-model ensemble hydrologic prediction using Bayesian model averaging , 2007 .

[45]  Rahmat-Allah Hooshmand,et al.  Short term electric load forecasting by wavelet transform and grey model improved by PSO (particle swarm optimization) algorithm , 2014 .