Climate change and global warming as the main human societies’ threats are fundamentally associated with energy consumption and GHG emissions. The residential sector, representing 27% and 17% of global energy consumption and CO2 emissions, respectively, has a considerable role to mitigate global climate change. Ten countries, including China, the US, India, Russia, Japan, Germany, South Korea, Canada, Iran, and the UK, account for two-thirds of global CO2 emissions. Thus, these countries’ residential energy consumption and GHG emissions have direct, significant effects on the world environment. The aim of this paper is to review the status and current trends of energy consumption, CO2 emissions and energy policies in the residential sector, both globally and in those ten countries. It was found that global residential energy consumption grew by 14% from 2000 to 2011. Most of this increase has occurred in developing countries, where population, urbanization and economic growth have been the main driving factors. Among the ten studied countries, all of the developed ones have shown a promising trend of reduction in CO2 emissions, apart from the US and Japan, which showed a 4% rise. Globally, the residential energy market is dominated by traditional biomass (40% of the total) followed by electricity (21%) and natural gas (20%), but the total proportion of fossil fuels has decreased over the past decade. Energy policy plays a significant role in controlling energy consumption. Different energy policies, such as building energy codes, incentives, energy labels have been employed by countries. Those policies can be successful if they are enhanced by making them mandatory, targeting net-zero energy building, and increasing public awareness about new technologies. However, developing countries, such as China, India and Iran, still encounter with considerable growth in GHG emissions and energy consumption, which are mostly related to the absence of strong, efficient policy.

A global review of energy consumption, CO2 emissions and policy in the residential sector (with an overview of the top ten CO2 emitting countries)

Energy is the lifeblood of modern societies. In the past decades, the world's energy consumption and associated CO 2 emissions increased rapidly due to the increases in population and comfort demands of people. Building energy consumption prediction is essential for energy planning, management, and conservation. Data-driven models provide a practical approach to energy consumption prediction. This paper offers a review of the studies that developed data-driven building energy consumption prediction models, with a particular focus on reviewing the scopes of prediction, the data properties and the data preprocessing methods used, the machine learning algorithms utilized for prediction, and the performance measures used for evaluation. Based on this review, existing research gaps are identified and future research directions in the area of data-driven building energy consumption prediction are highlighted.

A review of data-driven building energy consumption prediction studies

Achieving better energy-efficient air conditioning - A review of technologies and strategies

Predicting residential energy consumption using CNN-LSTM neural networks

Energy consumption forecasting for buildings has immense value in energy efficiency and sustainability research. Accurate energy forecasting models have numerous implications in planning and energy optimization of buildings and campuses. For new buildings, where past recorded data is unavailable, computer simulation methods are used for energy analysis and forecasting future scenarios. However, for existing buildings with historically recorded time series energy data, statistical and machine learning techniques have proved to be more accurate and quick. This study presents a comprehensive review of the existing machine learning techniques for forecasting time series energy consumption. Although the emphasis is given to a single time series data analysis, the review is not just limited to it since energy data is often co-analyzed with other time series variables like outdoor weather and indoor environmental conditions. The nine most popular forecasting techniques that are based on the machine learning platform are analyzed. An in-depth review and analysis of the ‘hybrid model’, that combines two or more forecasting techniques is also presented. The various combinations of the hybrid model are found to be the most effective in time series energy forecasting for building.

A review on time series forecasting techniques for building energy consumption

The considerable amount of energy consumption associated to the residential sector justifies and supports energy consumption modeling efforts. Among the three approaches to develop energy models, statistical approaches are a good option to avoid the burden associated to engineering approaches when observed/measured data is available. Among the statistical models, the linear regression analysis has shown promising results because of the reasonable accuracy and relatively simple implementation when compared to other methods. In this study, simple and multiple linear regression analysis along with a quadratic regression analysis were performed on hourly and daily data from a research house. The time interval of the observed data showed to be a relevant factor defining the quality of the model. Multiple linear regression models using the outdoor temperature and solar radiation offered improved coefficient of determination, but deteriorated root mean square error emphasizing the importance of using both parameters to assess and compare models. The content and structure of the paper has been devised to become a comprehensive material to be considered as the starting point for future work in this interesting research area. This paper also conveys the authors׳ belief that the future of residential energy forecasting is moving toward the development of individual models for each household due to the availability of data from smart meters, as well as the development of friendly and easy-to-use engineering software.

Regression analysis for prediction of residential energy consumption

Study of an aqueous lithium chloride desiccant system: air dehumidification and desiccant regeneration

Energy security, environmental concerns, thermal comfort, and economic matters are driving factors for the development of research on reducing energy consumption and the associated greenhouse gas emissions in every sector of the economy. Building energy consumption estimation has become a key approach to achieve the goals on energy consumption and emissions reduction. Energy performance of building is complicated since it depends on multiple variables associated to the building characteristics, equipment and systems, weather, occupants, and sociological influences. This paper aims to provide an up-to-date review on the basics of building energy estimation. Regarding models, a classification for energy estimation models is proposed based on the different classifications found in the literature review. The paper focuses on models developed with whole building energy simulation software and their validation. This focus is justified because of the importance that whole building energy tools have gained on areas such as green building design, and analysis of energy conservation strategies and retrofits. Since a suitable weather file is a major component for reliably simulations, the section about weather data provides pertinent information.

A review on the basics of building energy estimation

Methodology to estimate building energy consumption using EnergyPlus Benchmark Models

Heating and cooling energy requirements for buildings are usually supplied by separated systems such as furnaces or boilers for heating, and vapor compression systems for cooling. For these types of buildings, the use of combined cooling, heating, and power (CCHP) systems or combined heating and power (CHP) systems are an alternative for energy savings. Different researchers have claimed that the use of CCHP and CHP systems reduces the energy consumption related to transmission and distribution of energy. However, most of these analyses are based on reduction of operating cost without measuring the actual energy use and emissions reduction. The objective of this study is to analyze the performance of CCHP and CHP systems operating following the electric load (FEL) and operating following the thermal load (FTL), based on primary energy consumption (PEC), operation cost, and carbon dioxide emissions (CDE) for different climate conditions. Results show that CCHP and CHP systems operated FTL reduce the PEC for all the evaluated cities. On the other hand, CHP systems operated FEL always increases the PEC. The only operation mode that reduces PEC and CDE while reducing the cost is CHP-FTL. Copyright © 2009 John Wiley & Sons, Ltd.

Nelson Fumo

Papers

Regression analysis for prediction of residential energy consumption

Study of an aqueous lithium chloride desiccant system: air dehumidification and desiccant regeneration

A review on the basics of building energy estimation

Methodology to estimate building energy consumption using EnergyPlus Benchmark Models

Performance analysis of CCHP and CHP systems operating following the thermal and electric load