Evaluation of building energy demand forecast models using multi-attribute decision making approach

With the existence of several conventional and advanced building thermal energy demand forecast models to improve the energy efficiency of buildings,it is hard to find an appropriate,convenient,and efficient model.Evaluations based on statistical indexes(MAE,RMSE,MAPE,etc.)that characterize the accuracy of the forecasts do not help in the identification of the efficient building thermal energy demand forecast tool since they do not reflect the efforts entailed in implementation of the forecast model,i.e.,data collection to production/use phase.Hence,this work presents a Gini Index based Measurement of Alternatives and Ranking according to COmpromise Solution(GI-MARCOS),a hybrid Multi Attribute Decision Making(MADM)approach for the identification of the most efficient building energy demand forecast tool.GI-MARCOS employs(i)GI based objective weight method:assigns meaningful objective weights to the attributes in four phases(1:pre-processing,2:implementation,3:post-processing,and 4:use phase)thereby avoiding unnecessary biases in the expert’s opinion on weights and applicable to domains where there is a lack of domain expertise,and(ii)MARCOS:provides a robust and reliable ranking of alternatives in a dynamic environment.A case study with three alternatives evaluated over three to six attributes in four phases of implementation(pre-processing,implementation,post-processing and use)reveals that the use of GI-MARCOS improved the accuracy of alternatives MLR and BM by 6%and 13%,respectively.Moreover,additional validations state that(i)MLR performs best in Phase 1 and 2,while ANN performs best in Phase 3 and 4 with BM providing a mediocre performance in all four phases,(ii)sensitivity analysis:provides robust ranking with interchange of weights across phases and attributes,and(iii)rank correlation:ranks produce by GI-MARCOS has a high correlation with GRA(0.999),COPRAS(0.9786),and ARAS(0.9775).

Assessment of PV Shading Device on Building Energy Consumption Taking into Account Site Layout

This paper presents the results of a combined study of building energy consumption and the electricity production from PV modules integrated into a shading device, taking account of different site layouts. Various combinations of surrounding building configurations and the tilt angles of the shading device （that determines the PV module orientation） are examined.

Energy management of buildings with energy storage and solar photovoltaic:A diversity in experience approach for deep reinforcement learning agents

Deep reinforcement learning(DRL)is a suitable approach to handle uncertainty in managing the energy consumption of buildings with energy storage systems.Conventionally,DRL agents are trained by randomly selecting samples from a data set,which can result in overexposure to some data categories and under/no exposure to other data categories.Thus,the trained model may be biased towards some data groups and underperform(provide suboptimal results)for data groups to which it was less exposed.To address this issue,diversity in experience-based DRL agent training framework is proposed in this study.This approach ensures the exposure of agents to all types of data.The proposed framework is implemented in two steps.In the first step,raw data are grouped into different clusters using the K-means clustering method.The clustered data is then arranged by stacking the data of one cluster on top of another.In the second step,a selection algorithm is proposed to select data from each cluster to train the DRL agent.The frequency of selection from each cluster is in proportion to the number of data points in that cluster and therefore named the proportional selection method.To analyze the performance of the proposed approach and compare the results with the conventional random selection method,two indices are proposed in this study:the flatness index and the divergence index.The model is trained using different data sets(1-year,3-year,and 5-year)and also with the inclusion of solar photovoltaics.The simulation results confirmed the superior performance of the proposed approach to flatten the building’s load curve by optimally operating the energy storage system.

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.

OPTIMIZING WINDOW TO WALL RATIO FOR CONSERVING ENERGY IN OFFICE BUILDINGS FOR COOLING DOMINANT CLIMATES WITH AND WITHOUT DAYLIGHT UTILIZATION

The construction of fully glazed commercial building facades responsible for high energy consumption has become a common architectural practice worldwide irre­spective of the climate.This paper presents the methodology to optimize the Window to Wall Ratio(WWR)with and without daylight utilization to reduce energy con­sumption in office buildings for the climate of Lahore,Pakistan,using a simulation tool COMFEN.The impacts of solar heat and daylight entering through the build­ing façade with reference to different WWR and orientation were explored for the selection of optimum WWR.The optimum WWR was selected on the basis of least energy consumption whilst achieving a threshold lighting level.When daylight is not utilized,the energy demand is minimized by the lowest possible WWR.With daylight utilization,energy demand is optimized by use of WWRs of 13%to 30%according to orientation.Optimum WWR with daylight utilization offered a more balanced solution.The methodology used in this study can be applied to any location around the world to find optimum WWR for any glazing type.

CUBATURES FOR MEDIUM-SIZED PLUS ENERGY BUILDINGS

INTRODUCTION On the basis of dynamic building simulations within a maximal realistic framework,it may be useful with respect to the overall energy balance to dispense with pursuing a minimal surface/volume ratio of buildings-thus minimizing heat losses across the building shell-in favor of solar energy use.The specific use of the building(here:office or residential)plays a crucial role.Balancing the energy demand for heating and cooling and a possible photovoltaic yield,a surplus is possible in all cases under investigation.Long,low unobstructed buildings perform best due to large portions of roof area suitable for solar energy use.For tall buildings with less roof area,parts of the facades may be used for solar applications which makes them also perform better than compact designs.If the total energy demand including auxiliary energy for HVAC and especially electricity for the office and residential usages,respectively,is considered,compact cubatures of the size considered here(about 3500 m^(2))are not capable of providing positive energy balances.Residential usage performs worse than office use.Investigations are performed for the climatic conditions of Berlin,Germany.

Data-driven model predictive control for power demand management and fast demand response of commercial buildings using support vector regression

Demand response(DR)of commercial buildings by directly shutting down part of operating chillers could provide an immediate power reduction for power grids.In this special fast DR event,effective control needs to guarantee expected power reduction and ensure an acceptable indoor environment.This study,therefore,developed a data-driven model predictive control(MPC)using support vector regression(SVR)for fast DR events.According to the characteristics of fast DR events,the optimized hyperparameters of SVR and shortened searching range of genetic algorithm are used to improve the control performance.Meanwhile,a comprehensive comparison with RC-based MPC is conducted based on three scenarios of power demand controls.Test results show that the proposed SVR-based MPC could fulfill the control objectives of power demand and indoor temperature simultaneously.Compared with RC-based MPC,the SVR-based MPC could alleviate the time/labor cost of model development without sacrificing the control performance of fast DR events.

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.

The potential of remote sensing and GIS in urban building energy modelling

As the world continues to urbanize at an unprecedented rate,the energy demand in cities is rising.Buildings account for over 75%of all the energy consumed in cities and are responsible for over two-thirds of the emissions.Assessment of energy demand in buildings is a highly integrative endeavour,bringing together the interdisciplinary fields of energy and urban studies,along with a host of technical domains namely,geography,engineering,economics,sociology,and planning.In the last decade,several urban building energy modelling tools(UBEMs)have been developed for estimation as well as prediction of energy demand in cities.These models are useful in policymaking as they can evaluate future urban energy scenarios.However,data acquisition for generating the input database for UBEM has been a major challenge.In this review,a comprehensive assessment of the potential of remote sensing and GIS techniques for UBEM has been presented.Firstly,the most common input variables of UBEM have been identified by reviewing recent publications on UBEM and then studies related to the acquisition of data corresponding to these variables have been explored.More than 140 research papers and review articles relevant to remote sensing and GIS applications for building level data extraction in urban areas and UBEM applications have been investigated for this purpose.After going through level of details required for each of the input components of UBEM and studying the possibility of acquiring some of those data using remote sensing,it has been inferred that satellite remote sensing and Unmanned Aerial Vehicles(UAVs)have a strong potential in enhancing the input data space for UBEM but their applicability has been limited.Further,the challenges of the usage of these technologies and the possible solutions have also been presented in this study.It is recommended to utilise the existing methodologies of extracting information from remote sensing and GIS for UBEM,along with newer techniques such as machine learning and artificial intelligence.