Comparative study of machine learning-based multi-objective prediction framework for multiple building energy loads

Abstract Buildings are one of the significant sources of energy consumption and greenhouse gas emission in urban areas all over the world. Lighting control and building integrated photovoltaic (BIPV) are two effective measures in reducing overall primary energy consumption and carbon emission during building operation. Due to the complex energy nature of the building, accurate day-ahead prediction of heating, cooling, lighting loads and BIPV electrical power production is essential in building energy management. Owing to the changing metrological conditions, diversity and complexity of buildings, building energy load demands and BIPV electrical power production is highly variable. This may lead to poor building energy management, extra primary energy consumption or thermal discomfort. In this study, three machine learning-based multi-objective prediction frameworks are proposed for simultaneous prediction of multiple energy loads. The three machine learning techniques are artificial neural network, support vector regression and long-short-term-memory neural network. Since heating, cooling, lighting loads and BIPV electrical power production share similar affecting factors, it is computational time saving to adopt the proposed multi-objective prediction framework to predict multiple building energy loads and BIPV power production. The ANN-based predictive model results in the smallest mean absolute percentage error while SVM-based one cost the shortest computation time.

[1]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[2]  Jing Zhao,et al.  A hybrid method of dynamic cooling and heating load forecasting for office buildings based on artificial intelligence and regression analysis , 2018, Energy and Buildings.

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

[4]  C. van Treeck,et al.  Data-driven heating and cooling load predictions for non-residential buildings based on support vector machine regression and NARX Recurrent Neural Network: A comparative study on district scale , 2018, Energy.

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

[6]  Zhiwei Lian,et al.  Cooling Load Prediction Based on the Combination of Rough Set Theory and Support Vector Machine , 2006 .

[7]  Mithulananthan Nadarajah,et al.  Demand forecast of PV integrated bioclimatic buildings using ensemble framework , 2017 .

[8]  Eric Wai Ming Lee,et al.  Novel dynamic forecasting model for building cooling loads combining an artificial neural network and an ensemble approach , 2018, Applied Energy.

[9]  Giuseppina Ciulla,et al.  Modelling relationship among energy demand, climate and office building features: A cluster analysis at European level , 2016 .

[10]  Durga Toshniwal,et al.  Deep learning framework to forecast electricity demand , 2019, Applied Energy.

[11]  O. Perpiñán,et al.  PV power forecast using a nonparametric PV model , 2015 .

[12]  Peter Tzscheutschler,et al.  Day-ahead probabilistic PV generation forecast for buildings energy management systems , 2018, Solar Energy.

[13]  Rafik Belarbi,et al.  Analysis of thermal effects of vegetated envelopes: Integration of a validated model in a building energy simulation program , 2015 .

[14]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[15]  Biao Huang,et al.  A Long-Short Term Memory Recurrent Neural Network Based Reinforcement Learning Controller for Office Heating Ventilation and Air Conditioning Systems , 2017 .

[16]  Fan Yang,et al.  Effect of input variables on cooling load prediction accuracy of an office building , 2018 .

[17]  Amin Shahsavar,et al.  Prediction of energetic performance of a building integrated photovoltaic/thermal system thorough artificial neural network and hybrid particle swarm optimization models , 2019, Energy Conversion and Management.

[18]  Shahaboddin Shamshirband,et al.  Prediction of heat load in district heating systems by Support Vector Machine with Firefly searching algorithm , 2016 .

[19]  Qiang Zhang,et al.  Research on short-term and ultra-short-term cooling load prediction models for office buildings , 2017 .

[20]  Jin Yang,et al.  On-line building energy prediction using adaptive artificial neural networks , 2005 .

[21]  I. Santiago,et al.  Stochastic model for lighting's electricity consumption in the residential sector. Impact of energy saving actions , 2015 .

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

[23]  Moon Keun Kim,et al.  Predicting electricity consumption in a building using an optimized back-propagation and Levenberg–Marquardt back-propagation neural network: Case study of a shopping mall in China , 2018, Sustainable Cities and Society.

[24]  X. J. Luo,et al.  Development of integrated demand and supply side management strategy of multi-energy system for residential building application , 2019, Applied Energy.

[25]  Zhaojian Huang,et al.  Economic potential analysis of photovoltaic integrated shading strategies on commercial building facades in urban blocks: A case study of Colombo, Sri Lanka , 2020 .

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

[27]  Danny H.W. Li,et al.  Evaluation of lighting performance in office buildings with daylighting controls , 2001 .

[28]  Tony N.T. Lam,et al.  Artificial neural networks for energy analysis of office buildings with daylighting , 2010 .

[29]  Wei Gao,et al.  The feasibility of genetic programming and ANFIS in prediction energetic performance of a building integrated photovoltaic thermal (BIPVT) system , 2019 .

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

[31]  K. F. Fong,et al.  Development of multi-supply-multi-demand control strategy for combined cooling, heating and power system primed with solid oxide fuel cell-gas turbine , 2017 .

[32]  Tanveer Ahmad,et al.  Deployment of data-mining short and medium-term horizon cooling load forecasting models for building energy optimization and management , 2019, International Journal of Refrigeration.

[33]  Shahaboddin Shamshirband,et al.  Forecasting of consumers heat load in district heating systems using the support vector machine with a discrete wavelet transform algorithm , 2015 .

[34]  Xiaohui Zhou,et al.  Adaptive learning based data-driven models for predicting hourly building energy use , 2018 .

[35]  R. Chargui,et al.  Modeling of a residential house coupled with a dual source heat pump using TRNSYS software , 2014 .

[36]  Dragan Mitić,et al.  Appraisal of soft computing methods for short term consumers' heat load prediction in district heating systems , 2015 .

[37]  Dandan Liu,et al.  Prediction of building lighting energy consumption based on support vector regression , 2013, 2013 9th Asian Control Conference (ASCC).

[38]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[39]  Xin Wang,et al.  Prediction of energy consumption in hotel buildings via support vector machines , 2020 .

[40]  Khairul Salleh Mohamed Sahari,et al.  A novel hybrid modelling structure fabricated by using Takagi-Sugeno fuzzy to forecast HVAC systems energy demand in real-time for Basra city , 2020 .

[41]  Yongjun Sun,et al.  Development of clustering-based sensor fault detection and diagnosis strategy for chilled water system , 2019, Energy and Buildings.

[42]  Rizwan Ahmad,et al.  Intelligent techniques for forecasting electricity consumption of buildings , 2018, Energy.

[43]  Anh-Duc Pham,et al.  Predicting energy consumption in multiple buildings using machine learning for improving energy efficiency and sustainability , 2020 .

[44]  Radiša Jovanović,et al.  Ensemble of various neural networks for prediction of heating energy consumption , 2015 .

[45]  Xiaoning Xu,et al.  Using long short-term memory networks to predict energy consumption of air-conditioning systems , 2020 .

[46]  Yupeng Wu,et al.  Smart windows—Dynamic control of building energy performance , 2017 .

[47]  Fu Xiao,et al.  A short-term building cooling load prediction method using deep learning algorithms , 2017 .

[48]  José Luis Míguez,et al.  Calibrated simulation of a public library HVAC system with a ground-source heat pump and a radiant floor using TRNSYS and GenOpt , 2015 .

[49]  Xiaowei Luo,et al.  A Kalman filter-based bottom-up approach for household short-term load forecast , 2019, Applied Energy.

[50]  Joao Gari da Silva Fonseca Junior,et al.  Regional forecasts and smoothing effect of photovoltaic power generation in Japan: An approach with principal component analysis , 2014 .

[51]  Nora El-Gohary,et al.  Building Lighting Energy Consumption Prediction for Supporting Energy Data Analytics , 2016 .

[52]  Junqi Yu,et al.  Predictive model of energy consumption for office building by using improved GWO-BP , 2020 .

[53]  Pan Dongmei,et al.  Forecasting performance comparison of two hybrid machine learning models for cooling load of a large-scale commercial building , 2019, Journal of Building Engineering.

[54]  C. Montagud,et al.  Development and Experimental Validation of a TRNSYS Dynamic Tool for Design and Energy Optimization of Ground Source Heat Pump Systems , 2017 .

[55]  Xun Ma,et al.  Grid-Connected Semitransparent Building-Integrated Photovoltaic System: The Comprehensive Case Study of the 120 kWp Plant in Kunming, China , 2018 .

[56]  Xu Chen,et al.  A hybrid teaching-learning artificial neural network for building electrical energy consumption prediction , 2018, Energy and Buildings.

[57]  Shahaboddin Shamshirband,et al.  Application of support vector machine for prediction of electrical and thermal performance in PV/T system , 2016 .

[58]  Vivek Srikumar,et al.  Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks , 2018 .

[59]  V. I. Hanby,et al.  UK office buildings archetypal model as methodological approach in development of regression models for predicting building energy consumption from heating and cooling demands , 2013 .

[60]  Farraj F. Al-ajmi,et al.  Simulation of energy consumption for Kuwaiti domestic buildings , 2008 .

[61]  Merih Aydinalp,et al.  Modeling of the appliance, lighting, and space-cooling energy consumptions in the residential sector using neural networks , 2002 .