Online short-term forecast of greenhouse heat load using a weather forecast service

Abstract In some district heating systems, greenhouses represent a significant share of the total load, and can lead to operational challenges. Short term load forecast of such consumers has a strong potential to contribute to the improvement of the overall system efficiency. This work investigates the performance of recursive least squares for predicting the heat load of individual greenhouses in an online manner. Predictor inputs (weekly curves terms and weather forecast inputs) are selected in an automated manner using a forward selection approach. Historical load measurements from 5 Danish greenhouses with different operational characteristics were used, together with weather measurements and a weather forecast service. It was found that these predictors of reduced complexity and computational load performed well at capturing recurring load profiles, but not fast frequency random changes. Overall, the root mean square error of the prediction was within 8–20% of the peak load for the set of consumers over the 8 months period considered.

[1]  Christer Åhlund,et al.  Forecasting heat load for smart district heating systems: A machine learning approach , 2014, 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[2]  Brian Vad Mathiesen,et al.  4th Generation District Heating (4GDH) Integrating smart thermal grids into future sustainable energy systems , 2014 .

[3]  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 .

[4]  J. Ríos-Moreno,et al.  Greenhouse energy consumption prediction using neural networks models , 2009 .

[5]  Henrik Madsen,et al.  On flow and supply temperature control in district heating systems , 1994 .

[6]  Jose I. Bilbao,et al.  A review and analysis of regression and machine learning models on commercial building electricity load forecasting , 2017 .

[7]  Shahaboddin Shamshirband,et al.  Evaluation of the most influential parameters of heat load in district heating systems , 2015 .

[8]  Lars Arvastson.  Stochastic Modeling and Operational Optimization in District Heating Systems , 2001 .

[9]  A. Baille,et al.  Predicting the energy consumption of heated plastic greenhouses in south-eastern Spain , 2006 .

[10]  Fang Xu,et al.  Energy demand forecasting of the greenhouses using nonlinear models based on model optimized prediction method , 2016, Neurocomputing.

[11]  Olivier Jolliet,et al.  HORTICERN: an improved static model for predicting the energy consumption of a greenhouse , 1991 .

[12]  Abdul Hanan Abdullah,et al.  Heat load prediction in district heating systems with adaptive neuro-fuzzy method , 2015 .

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

[14]  M. Kozek,et al.  Online Short-Term Forecast of System Heat Load in District Heating Networks , 2011 .

[15]  Erik Dotzauer,et al.  Simple model for prediction of loads in district-heating systems , 2002 .

[16]  Thomas Mestekemper.  Energy demand forecasting and dynamic water temperature management , 2011 .

[17]  Mehmet Çunkas,et al.  Short-term load forecasting using fuzzy logic and ANFIS , 2015, Neural Computing and Applications.

[18]  Carlos E. Pedreira,et al.  Neural networks for short-term load forecasting: a review and evaluation , 2001 .

[19]  H. Madsen,et al.  Modelling the heat consumption in district heating systems using a grey-box approach , 2006 .

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

[21]  H. Madsen,et al.  Short-term heat load forecasting for single family houses , 2013 .

[22]  Frédéric Magoulès,et al.  A review on the prediction of building energy consumption , 2012 .

[23]  Julien Eynard,et al.  Wavelet-based multi-resolution analysis and artificial neural networks for forecasting temperature and thermal power consumption , 2011, Eng. Appl. Artif. Intell..

[24]  Niels Kjølstad Poulsen,et al.  Temperature prediction at critical points in district heating systems , 2009, Eur. J. Oper. Res..

[25]  orlov . . Nielsen Henrik Aalborg,et al.  Predicting the Heat Consumption in District Heating Systems using Meteorological Forecasts , 2000 .

[26]  Tao Hong,et al.  Improving short term load forecast accuracy via combining sister forecasts , 2016 .

[27]  Mohammad Javad Sanjari,et al.  Probabilistic Forecast of PV Power Generation Based on Higher Order Markov Chain , 2017, IEEE Transactions on Power Systems.

[28]  R. Campo,et al.  Adaptive Weather-Sensitive Short Term Load Forecast , 1987, IEEE Transactions on Power Systems.

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

[30]  Louise Trygg,et al.  Increased use of district heating in industrial processes – Impacts on heat load duration , 2009 .

[31]  C. Mondon,et al.  Predictive Control of a Complex District Heating Network , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[32]  Simon Sansregret,et al.  Load duration curve: A tool for technico-economic analysis of energy solutions , 2008 .

[33]  James D. Hamilton.  Time Series Analysis , 1994 .

[34]  R. Buizza,et al.  Neural Network Load Forecasting with Weather Ensemble Predictions , 2002, IEEE Power Engineering Review.

[35]  Ron Kohavi,et al.  Irrelevant Features and the Subset Selection Problem , 1994, ICML.

[36]  Julien Eynard,et al.  Optimal Control of a Multi-Energy District Boiler: A Case Study , 2011 .

[37]  Jonathan D. Cryer,et al.  Time Series Analysis , 1986, Encyclopedia of Big Data.

[38]  Fredrik Wernstedt,et al.  Multi-Agent Systems for Distributed Control of District Heating Systems , 2005 .

[39]  Magnus Dahl,et al.  Using ensemble weather predictions in district heating operation and load forecasting , 2017 .

[40]  Henrik Madsen,et al.  Models and Methods for Optimization of District Heating Systems.: Part II: Models and Control Methods , 1992 .

[41]  Christine W. Chan,et al.  Multiple neural networks for a long term time series forecast , 2004, Neural Computing & Applications.