Space heating and hot water demand analysis of dwellings connected to district heating scheme in UK

To achieve CO2 emissions reductions, the UK Building Regulations require developers of new residential buildings to calculate expected CO2 emissions arising from their energy consumption using a methodology such as Standard Assessment Procedure （SAP 2005） or, more recently SAP 2009. SAP encompasses all domestic heat consumption and a limited proportion of the electricity consumption. However, these calculations are rarely verified with real energy consumption and related CO2 emissions. This work presents the results of an analysis based on weekly heat demand data for more than 200 individual fiats. The data were collected from a recently built residential development connected to a district heating network. A method for separating out the domestic hot water （DHW） use and space heating （SH） demand has been developed and these values are compared to the demand calculated using SAP 2005 and SAP 2009 methodologies. The analysis also shows the variation in DHW and SH consumption with size of flats and with tenure （privately owned or social housing）. Evaluation of the space heating consumption also includes an estimate of the heating degree day （HDD） base temperature for each block of fiats and compares this to the average base temperature calculated using the SAP 2005 methodology.

Comparative study of univariate and multivariate strategy for short-term forecasting of heat demand density:Exploring single and hybrid deep learning models

Accurate short-term forecasting of heating energy demand is needed for achieving optimal building energy management,cost savings,environmental sustainability,and responsible energy consumption.Furthermore,short-term heating energy prediction contributes to zero-energy building performance in cold climates.Given the critical importance of short-term forecasting in heating energy management,this study evaluated six prevalent deep-learning algorithms to predict energy load,including single and hybrid models.The overall best-performing predictors were hybrid models using Convolutional Neural Networks,regardless of whether they were multivariate or univariate.Nevertheless,while the multivariate models performed better in the first hour,the univariate models often were more accurate in the final 24 h.Thus,the best-performing predictor of the first timestep was a multivariate hybrid Convolutional Neural Network–Recurrent Neural Network model with a coefficient of determination(R^(2))of 0.98 and the lowest mean absolute error.Yet,the best-performing predictor of the final timestep was the univariate hybrid model Convolutional Neural Network–Long Short-Term Memory with an R^(2)of 0.80.Also,the prediction accuracy of the best-performing multivariate hybrid models reduced faster per hour compared to the univariate models.These findings suggest that multivariate models may be better suited for early timestep predictions,while univariate models may be better suited for later time steps.Hence,combining the models can enhance accuracy at various timesteps for achieving high fidelity in forecasting and offering a comprehensive tool for energy management.

Sensitivity analysis to reduce duplicated features in ANN training for district heat demand prediction

Artificial neural network(ANN)has become an important method to model the nonlinear relationships between weather conditions,building characteristics and its heat demand.Due to the large amount of training data re-quired for ANN training,data reduction and feature selection are important to simplify the training.However,in building heat demand prediction,many weather-related input variables contain duplicated features.This paper develops a sensitivity analysis approach to analyse the correlation between input variables and to detect the variables that have high importance but contain duplicated features.The proposed approach is validated in a case study that predicts the heat demand of a district heating network containing tens of buildings at a university campus.The results show that the proposed approach detected and removed several unnecessary input variables and helped the ANN model to reduce approximately 20%training time compared with the traditional methods while maintaining the prediction accuracy.It indicates that the approach can be applied for analysing large num-ber of input variables to help improving the training efficiency of ANN in district heat demand prediction and other applications.

Enhancing hourly heat demand prediction through artificial neural networks:A national level case study

Meeting the goal of zero emissions in the energy sector by 2050 requires accurate prediction of energy consumption,which is increasingly important.However,conventional bottom-up model-based heat demand forecasting methods are not suitable for large-scale,high-resolution,and fast forecasting due to their complexity and the difficulty in obtaining model parameters.This paper presents an artificial neural network(ANN)model to predict hourly heat demand on a national level,which replaces the traditional bottom-up model based on extensive building simulations and computation.The ANN model significantly reduces prediction time and complexity by reducing the number of model input types through feature selection,making the model more realistic by removing non-essential inputs.The improved model can be trained using fewer meteorological data types and insufficient data,while accurately forecasting the hourly heat demand throughout the year within an acceptable error range.The model provides a framework to obtain accurate heat demand predictions for large-scale areas,which can be used as a reference for stakeholders,especially policymakers,to make informed decisions.

The energy performance and passive survivability of high thermal insulation buildings in future climate scenarios

Given that the passive performance simulation of buildings based on typical meteorological year data and specific design schemes makes it challenging to respond to climate change and refine design requirements on time,this article established a passive performance prediction model for future buildings considering multi-dimensional variables including climate change,building design,and operational characteristics.For high thermal insulation buildings under future climates,the mild climate zone is more sensitive than the others,cooling energy demand is more sensitive than heating demand,apartments are more sensitive than office buildings,and passive survivability is more sensitive than energy performance;for buildings of the same type located in the same climate zone,thermal design solutions determine the increase rate of cooling demand.The potential benefits of climate warming on heating demand reduction are almost zero,but the cooling demand increases significantly,with apartments and office buildings increasing up to 22.1% and 5.0%,respectively.Buildings generally overheat in the future,and the increase rate of the mild zone far exceeds other zones with duration and severity being 3004.8% and 877.7%for apartments,and 884.3% and 288.9%for office buildings,respectively.