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Demand forecast of PV integrated bioclimatic buildings using ensemble framework

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  • Raza, Muhammad Qamar
  • Nadarajah, Mithulananthan
  • Ekanayake, Chandima

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.

Suggested Citation

  • Raza, Muhammad Qamar & Nadarajah, Mithulananthan & Ekanayake, Chandima, 2017. "Demand forecast of PV integrated bioclimatic buildings using ensemble framework," Applied Energy, Elsevier, vol. 208(C), pages 1626-1638.
    • Handle: RePEc:eee:appene:v:208:y:2017:i:c:p:1626-1638
    DOI: 10.1016/j.apenergy.2017.08.192
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    References listed on IDEAS

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    1. Vu, D.H. & Muttaqi, K.M. & Agalgaonkar, A.P., 2015. "A variance inflation factor and backward elimination based robust regression model for forecasting monthly electricity demand using climatic variables," Applied Energy, Elsevier, vol. 140(C), pages 385-394.
    2. Giuseppe De Luca & Jan R. Magnus, 2011. "Bayesian model averaging and weighted-average least squares: Equivariance, stability, and numerical issues," Stata Journal, StataCorp LP, vol. 11(4), pages 518-544, December.
    3. Lü, Xiaoshu & Lu, Tao & Kibert, Charles J. & Viljanen, Martti, 2015. "Modeling and forecasting energy consumption for heterogeneous buildings using a physical–statistical approach," Applied Energy, Elsevier, vol. 144(C), pages 261-275.
    4. Cui, Can & Wu, Teresa & Hu, Mengqi & Weir, Jeffery D. & Li, Xiwang, 2016. "Short-term building energy model recommendation system: A meta-learning approach," Applied Energy, Elsevier, vol. 172(C), pages 251-263.
    5. Bahrami, Saadat & Hooshmand, Rahmat-Allah & Parastegari, Moein, 2014. "Short term electric load forecasting by wavelet transform and grey model improved by PSO (particle swarm optimization) algorithm," Energy, Elsevier, vol. 72(C), pages 434-442.
    6. Chou, Jui-Sheng & Ngo, Ngoc-Tri, 2016. "Time series analytics using sliding window metaheuristic optimization-based machine learning system for identifying building energy consumption patterns," Applied Energy, Elsevier, vol. 177(C), pages 751-770.
    7. Raza, Muhammad Qamar & Khosravi, Abbas, 2015. "A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1352-1372.
    8. Benedetti, Miriam & Cesarotti, Vittorio & Introna, Vito & Serranti, Jacopo, 2016. "Energy consumption control automation using Artificial Neural Networks and adaptive algorithms: Proposal of a new methodology and case study," Applied Energy, Elsevier, vol. 165(C), pages 60-71.
    9. Jain, Rishee K. & Smith, Kevin M. & Culligan, Patricia J. & Taylor, John E., 2014. "Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy," Applied Energy, Elsevier, vol. 123(C), pages 168-178.
    10. Li, Gong & Shi, Jing, 2010. "On comparing three artificial neural networks for wind speed forecasting," Applied Energy, Elsevier, vol. 87(7), pages 2313-2320, July.
    11. Raatikainen, Mika & Skön, Jukka-Pekka & Leiviskä, Kauko & Kolehmainen, Mikko, 2016. "Intelligent analysis of energy consumption in school buildings," Applied Energy, Elsevier, vol. 165(C), pages 416-429.
    12. Li, Xiwang & Wen, Jin & Bai, Er-Wei, 2016. "Developing a whole building cooling energy forecasting model for on-line operation optimization using proactive system identification," Applied Energy, Elsevier, vol. 164(C), pages 69-88.
    13. Sloughter, J. McLean & Gneiting, Tilmann & Raftery, Adrian E., 2010. "Probabilistic Wind Speed Forecasting Using Ensembles and Bayesian Model Averaging," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 25-35.
    14. Li, Qiong & Meng, Qinglin & Cai, Jiejin & Yoshino, Hiroshi & Mochida, Akashi, 2009. "Applying support vector machine to predict hourly cooling load in the building," Applied Energy, Elsevier, vol. 86(10), pages 2249-2256, October.
    15. Li, Gong & Shi, Jing, 2010. "Application of Bayesian model averaging in modeling long-term wind speed distributions," Renewable Energy, Elsevier, vol. 35(6), pages 1192-1202.
    16. Li, Gong & Shi, Jing & Zhou, Junyi, 2011. "Bayesian adaptive combination of short-term wind speed forecasts from neural network models," Renewable Energy, Elsevier, vol. 36(1), pages 352-359.
    17. Li, Yanting & He, Yong & Su, Yan & Shu, Lianjie, 2016. "Forecasting the daily power output of a grid-connected photovoltaic system based on multivariate adaptive regression splines," Applied Energy, Elsevier, vol. 180(C), pages 392-401.
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