On the Impact of Climate Change on Building Energy Consumptions: A Meta-Analysis

The body of literature on climate change impacts on building energy consumption is rising, driven by the urgency to implement adaptation measures. Nevertheless, the multitude of prediction methodologies, future scenarios, as well as climate zones investigated, results in a wide range of expected changes. For these reasons, the present review aims to map climate change impacts on building energy consumption from a quantitative perspective and to identify potential relationships between energy variation and a series of variables that could affect them, including heating and cooling degree-days (HDDs and CDDs), reference period, future time slices and IPCC emission scenarios, by means of statistical techniques. In addition, an overview of the main characteristics of the studies related to locations investigated, building types and methodological approaches are given. To sum up, global warming leads to: (i) decrease in heating consumptions; (ii) increase in cooling consumption; (iii) growth in total consumptions, with notable differences between climate zones. No strong correlation between the parameters was found, although a moderate linear correlation was identified between heating variation and HDDs, and total variation and HDDs. The great variability of the collected data demonstrates the importance of increasing specific impact studies, required to identify appropriate adaptation strategies.

Intelligent Prediction Method of Building Energy Consumption Based on Deep Learning

Building energy consumption prediction plays an important role in realizing building energy conservation control. Limited by some external factors such as temperature, there are some problems in practical applications, such as complex operation and low prediction accuracy. Aiming at the problem of low prediction accuracy caused by poor timing of existing building energy consumption prediction methods, a building energy consumption prediction and analysis method based on the deep learning network is proposed in this paper. Before establishing the energy consumption prediction model, the building energy consumption data source is preprocessed and analyzed. Then, based on the Keras deep learning framework, an improved long short-term memory (ILSTM) prediction model is built to support the accurate analysis of the whole cycle of the prediction network. At the same time, the adaptive moment (Adam) estimation algorithm is used to update and optimize the weight parameters of the model to realize the adaptive and rapid update and matching of network parameters. The simulation experiment is based on the actual dataset collected by a university in Southwest China. The experimental results show that the evaluation indexes MAE and RMSE of the proposed method are 0.015 and 0.109, respectively, which are better than the comparison method. The simulation experiment proves that the proposed method is feasible.

Introduction of ZEB Technology in Japan

It is necessary to reduce energy consumption in order to combat global warming and stabilize energy supply and demand. In particular, final energy consumption in the business sector (buildings such as office buildings and commercial facilities) accounted for about 16.1% of Japan\'s total in FY2018 database, an increase from about 12.6% in FY1990 database. Therefore, there is a need for the spread of zero-energy building (ZEB), which can significantly reduce the energy consumption in buildings. Since people are active in the building, energy consumption cannot be completely reduced to zero; however, it can be closer to ZEB by reducing the energy used in the building and creating energy in the building as much as possible. This chapter introduces some technologies of energy saving and energy creation to realize ZEB in general buildings in Japan.

Extracting Influential Factors for Building Energy Consumption via Data Mining Approaches

To effectively analyze building energy, it is important to utilize the environmental data that influence building energy consumption. This study analyzed outdoor and indoor data collected from buildings to find out the conditions of rooms that had a significant effect on heating and cooling energy consumption. To examine the conditions of the rooms in each building, the energy consumption importance priority was derived using the Gini importance of the random forest algorithm on external and internal environmental data. The conditions that had a significant effect on energy consumption were analyzed to be: (i) conditions related to the building design—wall, floor, and window area ratio, the window-to-wall ratio (WWR), the window-to-floor area ratio (WFR), and the azimuth, and (ii) the internal conditions of the building—the illuminance, occupancy density, plug load, and frequency of room utilization. The room conditions derived through analysis were considered in each sample, and the final influential building energy consumption factors were derived by using them in a decision tree as being the WFR, window area ratio, floor area ratio, wall area ratio, and frequency of use. Furthermore, four room types were classified by combining the room conditions obtained from the key factor classifications derived in this study.