Prediction Method And Model Of End-Use Energy Consumption For Regional Rural Residences In Northern China

The regional rural residences are comprised by a series of rural residences in town or city or province.The prediction of end-use energy consumption of rural residences(heating,cooking,household appliances,lighting and domestic hot water)at the regional scale is an significant basis for exploring energy-saving and emission reduction measures and achieving the high-intensity energy-saving and emission reduction targets of the rural residences at the regional scale.Presently,there is lack of relatively complete end-use energy consumption prediction model and method at district scale for rural residences in northern China.Thus,this paper focuses on the prediction model and method study of heating energy demand and energy consumption of each electrical terminal(including cooking energy consumption),with the research on the prediction model of cooking energy consumption(except for electric cookers)as the auxiliary.At present,the traditional heating energy demand prediction model for rural residences need the plenty number of input parameters and detailed information.Thus,the heating energy demand prediction model based on the limited parameters considering construction parameters and macro-economic and social factors simultaneously is very necessary.Because the law of electricity behavior of rural households is unclear,the traditional electrical end-use energy consumption prediction model ignored the influence of electricity behavior in different season,and cannot predict the hourly load.Therefore,it is necessary to establish the hourly electrical end-use energy consumption prediction model based on the electricity behavior.This model can effectively guide the government energy-saving policies,regional distribution,and has great significance for energy saving system configuration.Due to the difficulty for information acquisition of rural residences,plenty of influence factors,and macro-economic and social factors on the heating energy consumption should not be neglected,we proposed the prediction method of heating energy consumption of rural residences including the sampling and survey test method,construction method for the heating energy demand prediction model of rural residences——the three step variable selection method based on the quantitative indicators,and the calculation method of heating energy consumption of regional rural residences.The rationality and reliability of the construction method was verified based on the rural residences in Chifeng.The optimum combination of input parameters of heating energy demand prediction model was confirmed.The model was verified at the individual and regional scale respectively according to the measured data and existing research reports data.The results showed that the R2 calculated on the individual rural residence were 0.850.The predicted heating energy consumption for the InnerMongolia calculated by the heating energy consumption of individual rural residences of Chifeng is 20.15%higher than the energy consumption data in the research report on building energy conservation in China.To solve the uncertainty of the law of electricity utilization behavior in different seasons,this paper provides the combined method of noninvasive and invasive decomposition method to identify the use time of electrical equipment farmers in heating season and non-heating season.Taking Chifeng as an example,the operation law of electrical appliances is quantificationally analyzed.We found that the frequency of electric equipment for cooking in non-heating season is approximately two times than that in the heating season.Furthermore,we explored the major reason of rural residential electricity consumption differences,and found that the seasonal open of electrical equipment for cooking and the refrigerator or freezer cause the electricity consumption difference of rural residence in different seasons.For the problem that the traditional electricity energy consumption prediction model ignores the electricity behavior in different seasons,this paper established a prediction model of electricity terminal energy consumption of rural residences based on the randomness and seasonality of behavior.The model was divided into the seasonal segments.Take the 1083 rural residences in Chifeng as an example,the relative errors between the actual and estimated values of the total electricity consumption are below 10%.Compared with the traditional deterministic model,the prediction accuracy of the model we proposed is improved by more than 30%.In addition,the hourly electric load of rural residences under the different seasons was predicted and we found that the peak load of rural residences at district scale under the non-heating season is about 1.6 times than that of in the heating season.The results indicated that the power distribution of rural residence for regional scale should be carried out under different season.We established the cooking energy consumption prediction model(except the electrical cooking),and summarized the heating energy consumption of rural residences in Inner Mongolia based on the heating energy consumotion of individual rural residences of Chifeng.The heating energy consumption of rural residences in Inner Mongolia is 15.87 millio tce.The energy consumption flow chart of rural residences in Inner Mongolia was obtained.Finally,we applied the prediction method and models of the end-use energy consumption of regional rural residences in northern China proposed in this paper to predict the in the heating energy consumption,cooking energy consumption,domestic appliances,lighting,and hot water energy consumption in Hebei province.The predicted end-use energy consumption were validated with the multiple data from references.The energy consumption flow diagram of rural residences in Hebei was obtained and clearly shows the energy consumption ratio for terminal services.This paper put forwards the relatively complete set of end-use energy consumption prediction method and model of regional rural residence in northern China.The prediction method and model were verified to be reasonality and reliability in Inner Mongolia and Hebei.It can provide the theoretical data support for the energy planning and the energy system configuration.