A novel cost-optimizing demand response control for a heat pump heated residential building

The present article describes the integration of a data-driven predictive demand response control for residential buildings with heat pump and on-site energy generation. The data driven control approach schedules the heating system of the building. In each day, the next 24 hours heating demand of buildings, including space heating and domestic hot water consumption, are predicted by means of a hybrid wavelet transformation and a dynamic neural network. Linear programming is implemented to define a cost-optimal schedule for the heat pump operation. Moreover, the study discusses the impact of heat demand prediction error on performance of demand response control. In addition, the option of energy trading with the electrical grid is considered in order to evaluate the possibility of increasing the profit for private householders through on-site energy generation. The results highlight that the application of the proposed predictive control could reduce the heating energy cost up to 12% in the cold Finnish climate. Furthermore, on-site energy generation declines the total energy cost and consumption about 43% and 24% respectively. The application of a data-driven control for the demand prediction brings efficiency to demand response control.

[1]  Iakovos Michailidis,et al.  Joint energy demand and thermal comfort optimization in photovoltaic-equipped interconnected microgrids , 2015 .

[2]  David P. Morton,et al.  Optimal electricity rate structures for peak demand reduction using economic model predictive control , 2014 .

[3]  Fiorella Lauro,et al.  Model predictive control for building active demand response systems , 2015 .

[4]  P. O. Fanger,et al.  Thermal comfort: analysis and applications in environmental engineering, , 1972 .

[5]  Ricardo Enríquez,et al.  Solar Forecasting Requirements for Buildings MPC , 2016 .

[6]  Henrik Madsen,et al.  A model predictive control strategy for the space heating of a smart building including cogeneration of a fuel cell-electrolyzer system , 2014 .

[7]  Gordon Lightbody,et al.  Modelling and disturbance estimation for model predictive control in building heating systems , 2016 .

[8]  Farid Atry,et al.  Multi-step ahead forecasts for electricity prices using NARX: A new approach, a critical analysis of one-step ahead forecasts , 2009 .

[9]  Biswajit Basu,et al.  Cooperative optimization of building energy systems in an economic model predictive control framework , 2016 .

[10]  Claudia Schremmer.  Multimedia applications of the wavelet transform , 2001 .

[11]  Ahmad Lotfi,et al.  Behavioural pattern identification and prediction in intelligent environments , 2013, Appl. Soft Comput..

[12]  Barbara Mayer,et al.  Management of hybrid energy supply systems in buildings using mixed-integer model predictive control , 2015 .

[13]  Alistair B. Sproul,et al.  Development of hybrid numerical and statistical short term horizon weather prediction models for building energy management optimisation , 2015 .

[14]  E. McKenna,et al.  Demand response behaviour of domestic consumers with photovoltaic systems in the UK: an exploratory analysis of an internet discussion forum , 2014 .

[15]  Martin Schmelas,et al.  Adaptive predictive control of thermo-active building systems (TABS) based on a multiple regression algorithm: First practical test , 2016 .

[16]  Hossein Afshari,et al.  Field tests of an adaptive, model-predictive heating controller for residential buildings , 2015 .

[17]  Juha Jokisalo,et al.  Influence of energy demand response actions on thermal comfort and energy cost in electrically heated residential houses , 2017 .

[18]  W. Beckman,et al.  Solar Engineering of Thermal Processes , 1985 .

[19]  Farkhondeh Jabari,et al.  Optimal short-term scheduling of a novel tri-generation system in the presence of demand response programs and battery storage system , 2016 .

[20]  Antonio Vicino,et al.  Receding horizon control for demand-response operation of building heating systems , 2014, 53rd IEEE Conference on Decision and Control.

[21]  Massimiliano Scarpa,et al.  Demand response management by means of heat pumps controlled via real time pricing , 2015 .

[22]  Jung-Ho Huh,et al.  Development of a method of real-time building energy simulation for efficient predictive control , 2016 .

[23]  J. Jokisalo,et al.  Development of weighting factors for climate variables for selecting the energy reference year according to the EN ISO 15927-4 standard , 2012 .

[24]  N. Nwulu,et al.  Implementing a model predictive control strategy on the dynamic economic emission dispatch problem with game theory based demand response programs , 2015 .

[25]  Matti Lehtonen,et al.  Combining the Demand Response of direct electric space heating and partial thermal storage using LP optimization , 2014 .

[26]  Shengwei Wang,et al.  A Novel Air-conditioning System for Proactive Power Demand Response to Smart Grid☆ , 2014 .