Publications
International Journal of Advanced Network, Monitoring and Controls
Xi'an Technological University
Subject: Computer Science, Software Engineering
eISSN: 2470-8038
SEARCH WITHIN CONTENT
Prediction of the Heat Load in Central Heating Systems Using GA-BP Algorithm
Bingqing Guo / Jin Xu / Ling Cheng / Lei Chen
Keywords : Central heating, Heat load, BP network, GA-BP network, Heating network
Citation Information : International Journal of Advanced Network, Monitoring and Controls. Volume 2, Issue 4, Pages 137-141, DOI: https://doi.org/10.1109/iccnea.2017.90
License : (CC BY-NC-ND 4.0)
Published Online: 09-April-2018
- ARTICLE
- FIGURES & TABLES
- REFERENCES
- EXTRA FILES
- COMMENTS
ARTICLE
ABSTRACT
This paper presented the research on heat load prediction method of central heating system. The combined simulation data at Xi'an in January was used as the samples for training and predicting. This paper selected the daily average outdoor wind speed, the daily average outdoor temperature, date type, sunshine duration as input variables and the heating load value as output variable. After preprocessing of the historical data, the BP neural network algorithm and the GA-BP algorithm were employed to predict and verify heat load respectively. Based on the analysis of prediction results, it showed that the error between the predicted data and the actual value using the BP algorithm is large (maximum:-39.8%) and not suitable for heating load prediction while the error between the predicted data and the actual value using the GA-BP algorithm is small (maximum:-16.6%) and within the acceptable range. This paper provided a feasible method for heating load prediction.
FIGURES & TABLES
REFERENCES
D. Böttger, M. Götz, N. Lehr, Hendrik Kondziellaa, Thomas Bruckner Potential of the power-to-heat technology in district heating grids in Germany. Energy Procedia, 46 (2014), pp. 246–253.
M.A. Ancona, M. Bianchi, L. Branchini, F. Melino District heating network design and analysis. Energy Procedia, 45 (2014), pp. 1225– 1234.
A. Gebremedhin. Optimal utilization of heat demand in district heating system—a case study. Renewable Sustainable Energy Rev, 30 (2014), pp. 230–236.
L. Di Luciaa, K. Ericssona. Low-carbon district heating in Sweden— examining a successful energy transition. Energy Res Soc Sci, 4 (2014), pp. 10–20.
S.Werner, The heat load in district healing system. Swenden: Chalmers University of Technology, 1984
L.Arvastson, Stochastic modeling and operational optimization in district heating systems. Lund University, 2001
E.Dotzauer, ―Simple model for prediction of loads in district-heating systems,‖ Applied Energy, vol. 73, 2008, pp. 277-284, doi
Bacher P, Madsen H, Nielsen HA. ―Online short-term heat load forecasting for single family houses,‖ Proc. 39th Annual Conference of the IEEE Industrial Electronics Society, IEEE Press,, 2013,pp.5741-5746
Stevanovic VD, Zivkovic B, Prica S, et al. Prediction of thermal transients in district heating systems. Energy Conversion and Management, 2014, 50(9): 2167-2173
Wojdyga K. An influence of weather conditions on heat demand in district heating systems. Energy and Buildings, 2015, 40(11): 2009- 2014
Ohlsson MB, Peterson CO, Pi H, et al. Predicting System Loads with Artificial Neural Networks--Methods and Results from" The Great Energy Predictor Shootout". ASHRAE Transactions-American Society of Heating Refrigerating Airconditioning Engin, 1994, 100(2): 1063-1074
Stevenson WJ. Using artificial neural nets to predict building energy parameters]. ASHRAE Transactions., 2014, 100(3): 1076-1087
Feuston BP, Thurtell JH. Generalized nonlinear regression with ensemble of neural nets: the great energy predictor shoot out. ASHRAE Transactions, 2012, (5): 1068-1080