Updating the PECAS Modeling Framework to Include Energy Use Data for Buildings

This study investigates the consumption of electricity and natural gas for building operations for several categories of residential and non-residential buildings. The study updates the Production Exchange Consumption Allocation System (PECAS) land use modeling framework to include energy components. An energy database was assembled to study energy consumption in buildings. The authors conducted statistical analysis of utility data and estimated linear regression models to predict energy consumption in buildings. Results are validated using data from independent sources, including the California Residential Appliance Saturation Study (RASS) and the Commercial End-Use Survey (CEUS). Results are used to update PECAS and form part of the baseline study to estimate energy and greenhouse gas balances in an urban metabolism framework for the analysis of the environmental impacts of complex urban regions. The results also allow the total energy consumption and greenhouse gas emissions for residential and commercial building operations to be estimated through the application to the total residential and commercial building inventory in the region. These results are then useful for the evaluation of possible energy savings in buildings

A Review of Electricity Demand Forecasting in Low and Middle Income Countries: The Demand Determinants and Horizons

With the globally increasing electricity demand, its related uncertainties are on the rise as well. Therefore, a deeper insight of load forecasting techniques for projecting future electricity demands becomes imperative for business entities and policy makers. The electricity demand is governed by a set of different variables or “electricity demand determinants”. These demand determinants depend on forecasting horizons (long term, medium term, and short term), the load aggregation level, climate, and socio-economic activities. In this paper, a review of different electricity demand forecasting methodologies is provided in the context of a group of low and middle income countries. The article presents a comprehensive literature review by tabulating the different demand determinants used in different countries and forecasting the trends and techniques used in these countries. A comparative review of these forecasting methodologies over different time horizons reveals that the time series modeling approach has been extensively used while forecasting for long and medium terms. For short term forecasts, artificial intelligence-based techniques remain prevalent in the literature. Furthermore, a comparative analysis of the demand determinants in these countries indicates a frequent use of determinants like the population, GDP, weather, and load data over different time horizons. Following the analysis, potential research gaps are identified, and recommendations are provided, accordingly

Distinguishing cause from effect using observational data: methods and benchmarks

The discovery of causal relationships from purely observational data is a fundamental problem in science. The most elementary form of such a causal discovery problem is to decide whether X causes Y or, alternatively, Y causes X, given joint observations of two variables X, Y. An example is to decide whether altitude causes temperature, or vice versa, given only joint measurements of both variables. Even under the simplifying assumptions of no confounding, no feedback loops, and no selection bias, such bivariate causal discovery problems are challenging. Nevertheless, several approaches for addressing those problems have been proposed in recent years. We review two families of such methods: Additive Noise Methods (ANM) and Information Geometric Causal Inference (IGCI). We present the benchmark CauseEffectPairs that consists of data for 100 different cause-effect pairs selected from 37 datasets from various domains (e.g., meteorology, biology, medicine, engineering, economy, etc.) and motivate our decisions regarding the "ground truth" causal directions of all pairs. We evaluate the performance of several bivariate causal discovery methods on these real-world benchmark data and in addition on artificially simulated data. Our empirical results on real-world data indicate that certain methods are indeed able to distinguish cause from effect using only purely observational data, although more benchmark data would be needed to obtain statistically significant conclusions. One of the best performing methods overall is the additive-noise method originally proposed by Hoyer et al. (2009), which obtains an accuracy of 63+-10 % and an AUC of 0.74+-0.05 on the real-world benchmark. As the main theoretical contribution of this work we prove the consistency of that method.Comment: 101 pages, second revision submitted to Journal of Machine Learning Researc

Energy consumption, economic growth, and CO2 emissions in G20 countries: Application of adaptive neuro-fuzzy inference system

Understanding the relationships among CO2 emissions, energy consumption, and economic growth helps nations to develop energy sources and formulate energy policies in order to enhance sustainable development. The present research is aimed at developing a novel efficient model for analyzing the relationships amongst the three aforementioned indicators in G20 countries using an adaptive neuro-fuzzy inference system (ANFIS) model in the period from 1962 to 2016. In this regard, the ANFIS model has been used with prediction models using real data to predict CO2 emissions based on two important input indicators, energy consumption and economic growth. This study made use of the fuzzy rules through ANFIS to generalize the relationships of the input and output indicators in order to make a prediction of CO2 emissions. The experimental findings on a real-world dataset of World Development Indicators (WDI) revealed that the proposed model efficiently predicted the CO2 emissions based on energy consumption and economic growth. The direction of the interrelationship is highly important from the economic and energy policy-making perspectives for this international forum, as G20 countries are primarily focused on the governance of the global economy

Energy Consumption, Economic Growth, and CO2 Emissions in G20 Countries: Application of Adaptive Neuro-Fuzzy Inference System

Understanding the relationships among CO2 emissions, energy consumption, and economic growth helps nations to develop energy sources and formulate energy policies in order to enhance sustainable development. The present research is aimed at developing a novel efficient model for analyzing the relationships amongst the three aforementioned indicators in G20 countries using an adaptive neuro-fuzzy inference system (ANFIS) model in the period from 1962 to 2016. In this regard, the ANFIS model has been used with prediction models using real data to predict CO2 emissions based on two important input indicators, energy consumption and economic growth. This study made use of the fuzzy rules through ANFIS to generalize the relationships of the input and output indicators in order to make a prediction of CO2 emissions. The experimental findings on a real-world dataset of World Development Indicators (WDI) revealed that the proposed model efficiently predicted the CO2 emissions based on energy consumption and economic growth. The direction of the interrelationship is highly important from the economic and energy policy-making perspectives for this international forum, as G20 countries are primarily focused on the governance of the global economy.This research was funded by Universiti Teknologi Malaysia (UTM), Flagship UTMSHINE grant PY/2017/02187

Estimating dynamic consumption of antibiotics using panel data: the shadow effect of bacterial resistance

To some extent, antibiotics are similar to addictive goods since current consumption is reinforced by past use because of bacterial resistance, which represents a growing concern in many countries. The purpose of this paper is to explore how consumers adjust their current level of antibiotic consumption towards desired levels over time. We construct a balanced panel dataset (2000-2007) for 20 Italian regions and estimate a dynamic model where antibiotic consumption depends upon demographic and socioeconomic characteristics of the population, the supply of health care in the community, antibiotic price, and the "capital stock" of endogenous bacterial resistance measured by past consumption. We apply alternative dynamic estimators for short panels: the bias-corrected least squares dummy variable (LSDVC) and the system Blundell-Bond GMM estimator (GMM-BB). The estimation results are stable across different model specifications and show that antibiotic use in previous periods has a positive impact on current antimicrobial consumption (between 0.14 and 0.39). This indicates that the process of adjustment to desired levels of consumption is relatively fast (approximately 1.2-1.6 years). Weak persistence in consumption may suggest that individuals are responsive to changes in antibiotic effectiveness.Antibiotic consumption, bacterial resistance, dynamic model

Forecasting Nigeria\u27s Electricity Demand and Energy Efficiency Potential Under Climate Uncertainty

The increasing population and socio-economic growth of Nigeria, coupled with the current, unmet electricity demand, requires the need for power supply facilities expansion. Of all Nigeria’s electricity consumption by sector, the residential sector is the largest and growing at a very fast rate. To meet this growing demand, an accurate estimation of the demand into the future that will guide policy makers to adequately plan for the expansion of electricity supply and distribution, and energy efficiency standards and labeling must be made. To achieve this, a residential electricity demand forecast model that can correctly predict future demand and guide the construction of power plants including cost optimization of building these power infrastructures is needed. Modelling electricity demand in developing countries is problematic because of scarcity of data and methodologies that adequately consider detailed disaggregation of household appliances, energy efficiency improvements, and stock uptakes. This dissertation addresses these gaps and presents methodologies that can carry out a detailed disaggregation of household appliances, a more accurate electricity demand projection, peak load reduction, energy savings, economic, and environmental benefits of energy efficiency in the residential sector of Nigeria. This study adopts a bottom-up and top-down approach (hybrid) supplemented with hourly end-use demand profile to model residential electricity consumption. and project efficiency improvement through the introduction of energy efficiency standards and labelling (EE S&L) under two scenarios (Business As Usual and Best Available Technology). A consumer life-cycle cost analysis was also conducted to determine the cost-effectiveness of introducing EE S& L to consumers. The results show significant savings in energy and carbon emissions, increased cooling demand due to climate uncertainty, and negative return on investment and increase lifecycle costs to consumers who purchase more efficient appliances. These results are subject to some level of uncertainties that are mainly caused by the input data. The uncertainties were analyzed based on a Monte Carlo Simulation. The uncertainties that were considered including the type of distributions applied to them were outlined and the result of the outputs were presented

A novel machine learning approach for identifying the drivers of domestic electricity users’ price responsiveness

Time-based pricing programs for domestic electricity users have been effective in reducing peak demand and facilitating renewables integration. Nevertheless, high cost, price non-responsiveness and adverse selection may create the possible challenges. To overcome these challenges, it can be fruitful to investigate the ‘high-potential’ users, which are more responsive to price changes and apply time-based pricing to these users. Few studies have investigated how to identify which users are more price-responsive. We aim to fill this gap by comprehensively identifying the drivers of domestic users’ price responsiveness, in order to facilitate the selection of the high-potential users. We adopt a novel data-driven approach, first by a feed forward neural network model to accurately determine the baseline monthly peak consumption of individual households, followed by an integrated machine-learning variable selection methodology to identify the drivers of price responsiveness applied to Irish smart meter data from 2009-10 as part of a national Time of Use trial. This methodology substantially outperforms traditional variable selection methods by combining three advanced machine-learning techniques. Our results show that the response of energy users to price change is affected by a number of factors, ranging from demographic and dwelling characteristics, psychological factors, historical electricity consumption, to appliance ownership. In particular, historical electricity consumption, income, the number of occupants, perceived behavioural control, and adoption of specific appliances, including immersion water heater and dishwasher, are found to be significant drivers of price responsiveness. We also observe that continual price increase within a moderate range does not drive additional peak demand reduction, and that there is an intention-behaviour gap, whereby stated intention does not lead to actual peak reduction behavior. Based on our findings, we have conducted scenario analysis to demonstrate the feasibility of selecting the high potential users to achieve significant peak reduction

Disentangling spillover effects of antibiotic consumption: a spatial panel approach

Literature on socioeconomic determinants of antibiotic consumption in the community is limited to few countries using cross-sectional data. This paper analyses regional variations in outpatient antibiotics in Italy using a balanced panel dataset covering the period 2000-2008. We specify an econometric model where antibiotic consumption depends upon demographic and socioeconomic characteristics of the population, the supply of health care services in the community, and antibiotic copayments. The model is estimated by means of Ordinary least squares techniques with fixed effects (FE). The implications of consumption externalities across geographical areas are investigated by means of spatial-lag and spatial-error models (SLFE and SEFE). We find significant and positive income elasticity and negative effects of copayments. Antibiotic use is also affected by the age structure of the population and the supply of community health care. Finally, we find evidence of spatial dependency in the use of antibiotics across regions. This suggests that regional policies (e.g. public campaigns) aimed at increasing efficiency in antibiotic consumption and controlling bacterial resistance may be influenced by policy makers in neighbouring regions. There will be scope for a strategic and coordinated view of regional policies towards the use of antibiotics.Antibiotic consumption, Socioeconomic inequalities, Spatial dependency, Regional policies.

Public bus transport demand elasticities in India

A number of static and dynamic specifications of a log linear demand function for public transport are estimated using aggregate panel data for 22 Indian states over the period 1990 to 2001. Demand has been defined as total passenger kilometers to capture actual market transactions, while the regressors include public transit fare, per capita income, service quality, and other demographic and social variables. In all cases, transit demand is significant and inelastic to the fare. Service quality is the most significant policy variable. Finally, social and demographic variables highlight the complex nature of public bus transit demand in India.Demand Elasticities, Dynamic Panel Data, Bus Transport, India