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While energy efficiency projects could partly meet new energy demand more cheaply than new supplies, weak economic institutions in developing and transitional economies impede developing and financing energy efficiency retrofits. This book analyzes these difficulties, suggests a 3-part model for projectizing and financing energy efficiency retrofits, and presents thirteen case studies to illustrate the issues and principles involved.

(1) Background: Millions of U.S. households experience energy insecurity, defined as the inability to adequately meet household energy needs. (2) Objectives: Examine the relationship between different dimensions of energy insecurity and adverse mental health (anxiety and depression) and assess whether these associations vary by household income. (3) Methods: This study investigates the relationship between energy insecurity, income, and mental health (anxiety and depression) using 2022 and 2023 data from the U.S. Census Bureau’s Household Pulse Survey. (4) Results: Adverse mental health is more closely related to behavioral responses to energy insecurity rather than the economic burden of energy insecurity and are on par with food insecurity. Adverse mental health associations with keeping the home at an unhealthy temperature and giving up basic necessities to pay an energy bill are particularly large compared to being unable to pay an energy bill in full. For those without energy insecurity, the probability of adverse mental health outcomes decreases as income increases. For those with energy insecurity, the probability of adverse mental health outcomes is high across all income groups. This study underscores the need to consider economic and behavioral dimensions of energy insecurity in discussions about mental health.

The identification of the population suffering from energy poverty, which is more visible after Covid-19 pandemic following by the 2021 energy crisis, is an essential requirement for producing systematic and sustainable solutions. Although European Union approaches to the problem with a multi-indicator sets; this indicator sets have a large amount of secure and almost unreachable data, such as identity information, wage information, health information, asset information (title deed, rental income), expenditure information, debt information, credit information, bank records, etc. Experienced two long term projects between 2014 and 2016 (problem definition for energy theft and the best practices searching 13 different country examples including Brazil, Hungary, India, etc.) and 2016–2018 (energy poverty set and consumption characteristics in Turkey) over 6 million end-user consumption and payment data brings us to confirm that. The primary indicator of energy poverty is the arrears on utility bills. The arrears resulting from the affordability problem of the energy consumed trigger a power cut-off job order in the utility company. This research examines the literature and country social assistance implementation data to see how an energy poverty level can be identified using details on arrears and powercut job orders. On this subject, power-cut job orders were constituted, because of arrears on utility bills, were subjected to statistical analysis, and the compatibility of the trend data with the socio-economic development index was investigated. Cities with a less indexes have more utility bill arrears in terms of both number and volume, according to correlation-test data. Urban cities are more visible in data since the non-urbanized cities have some energy theft activities which show us no efficiency target for the consumption! Hence one of the strategical step for decreasing the non-technical losses is having more registered customer, the relationship between the growth index and the number of customers is another intriguing finding. Separating the consumption levels of arrears, it is found that 63% of total non-payment is depending on 18% of consumers. Trend analysis confirmed that every energy consumption level has the absolute and fluctuated component inside. The number of people inside the absolute poverty cluster is coherent with national and international approaches almost in the same number. The findings revealed that arrears on utility bills can be used specifically to assess the population identified with energy dependency rather than relying on evidence from a variety of sources.

Highly energy-efficient buildings have generated remarkable interest over the last few years. There is a need for simulation based effective control systems for efficient usage of electrical and fossil fuel driven devices, as they contribute to energy-efficient buildings and assist in gaining flexibility for the human occupancy-based energy loads. In this context, the integrated energy profile of a building can be ascertained by effective research approaches, as this knowledge would be beneficial to understand the demographics with respect to human occupancy and activities, as well as estimate varying energy consumption over time. Utility data from Smart Meter (SM) readings can reveal detailed information that could be mapped to predict resident occupancy and the usage patterns of specific types of appliances over desired time intervals. This research develops a user-driven simulation tool with realistic data acquisition options and assumptions of potential human behavior to determine energy usage patterns over time without the utility billing information. In this work, factors such as level of human occupancy, the possibility of space being occupied, thermostat settings, building envelope infrastructural aspects, types of appliances used in households, appliance energy related capacities, and the probability of using each appliance is considered, along with variance in weather, and heating-cooling systems specifications. For five specific benchmarked scenarios, the range of the random numbers is specified based on assumed potential human behavior for occupancy and energy-consuming appliances usage probabilities, with respect to the time of the day, weekday, and weekends. The simulation is developed using the Visual Basic Application (VBA)® in Microsoft Excel®, based on the discrete-event Monte Carlo Simulation (MCS). The simulated energy usage and the cost are reflected in the sensitivity analysis by comparing factors such as the level of human occupancy, appliance type, and time intervals.

End-use consumption provides more detailed information than total consumption and reveals the mechanism of energy flow through a given building. Specifically, for weather-sensitive energy end-uses, it enables the prioritization and selection of heating and cooling areas requiring investigation and actions. One of the major barriers to acquiring such heating and cooling information for small- and medium-sized buildings or low-income households is the high cost related to submetering and maintenance. The end-use data, especially for heating and cooling end-uses, of such-sized buildings are a national blind spot. In this study, to alleviate this measurement cost problem, two weather-sensitive energy disaggregation methods were examined: the simplified weather-related energy disaggregation (SED) and change-point regression (CPR) methods. The first is a nonparametric approach based on heuristics, whereas the second is a parametric approach. A comparative analysis (one-way ANOVA, correlation analysis, and individual comparison) was performed to explore the disaggregation results regarding heating and cooling energy perspectives using a measurement dataset (MEA) from eleven office buildings. The ANOVA results revealed that there was no significant difference between the three groups (SED, CPR, and MEA); rather strong correlation was observed (r > 0.95). Furthermore, an analysis of the building-level comparison showed that the more distinct the seasonal usage in the monthly consumption pattern, the lower the estimation error. Thus, the two approaches appropriately estimated the amount of heating and cooling used compared with the measurement dataset and demonstrated the possibility of mutual complements.

Abstract The rapidly expanding electrical sector urgently requires more effective strategies for electricity fee management. This article designs and implements an intelligent billing management system based on the service-oriented architecture. Additionally, this research proposes a predictive model based on deep learning that integrates a cascaded model utilizing particle swarm optimization, self-organizing maps, and bidirectional gated recurrent units algorithms to accurately forecast electricity revenue and refund scenarios. Experimental results demonstrate the superior accuracy and efficiency of this integrated model.

This study is a product of the Africa Infrastructure Country Diagnostic (AICD), a project designed to expand the world's knowledge of physical infrastructure in Africa. The AICD provides a baseline against which future improvements in infrastructure services can be measured, making it possible to monitor the results achieved from donor support. It also offers a more solid empirical foundation for prioritizing investments and designing policy reforms in the infrastructure sectors in Africa. The book draws upon a number of background papers that were prepared by World Bank staff and consultants, under the auspices of the AICD. The main findings were synthesized in a flagship report titled Africa's infrastructure: A time for transformation, published in November 2009. Meant for policy makers, that report necessarily focused on the high-level conclusions. It attracted widespread media coverage feeding directly into discussions at the 2009 African union commission heads of state summit on infrastructure.

Poorly implemented energy subsidies are economically costly to taxpayers and damage the environment. This report aims at providing the emerging lessons form a representative sample of case studies in 20 developing countries that could help policy makers to address implementation challenges, including overcoming political economy and affordability constraints. The sample has selected on the basis of a number of criteria, including the country’s level of development (and consumption), developing country region, energy security and the fuel it subsidies (petroleum fuel, electricity, natural gas). The case studies were supported by data collection related to direct budgetary subsidies, fuel and electricity tariffs, and household survey data.The analysis provides strong evidence of the success of reforms in reducing the associated fiscal burden. For the sample of countries, the average energy subsidy recorded in the budget was reduced from 1.8% in 2004 to 1.3%GDP in 2010. The reduction of subsidies is particularly remarkable for net energy importers. Pass-through of international fuel prices was also notable in the case of electricity generated by fossil fuel. For the sample of countries, the average end-user electricity tariff increased by 50%, from USD 6 cents in 2002 to USD 9 cents per kWh in 2010.In spite of the relatively price inelastic demand for gasoline and diesel, fossil fuel consumption in the road sector (per unit of GDP) declined in the 20 countries examined from 53 (44) in 2002 to about 23 kt oil equivalent per million of GDP in 2008 in the case of gasoline (Diesel). The most notable decline in consumption was recorded in the low and lower middle income countries. This reflects the much higher rate of growth in GDP in this group of countries and underlines the opportunities to influence future consumption behavior rather than modifying the existing consumption patterns, overcoming inertia and vested interests. Similar trends are recorded for power consumption.While there is no one-size-fits-all model for subsidy reform, implementation of compensatory social policies and an effective communication strategy, before the changes are introduced, reduces helped with the implementation of reforms.

This book explores energy savings performance contracts (ESPCs) as a means of overcoming some of the more difficult hurdles in promoting energy efficiency in public facilities. ESPCs represent a very attractive solution to many of the problems that are unique to public agencies, since they involve outsourcing a full project cycle to a service provider. From the detailed audit through implementation and savings verification, ESPCs can relieve public agencies of bureaucratic hassles, while service providers can secure the off-budget project financing and be paid from the actual energy savings, thus internalizing project performance risks. ESPC bidding also allows public agencies to select from a range of technical solutions, maximizing the benefit to the agency. Global experience suggests that ESPCs have been more effective at realizing efficiency gains than many other policy measures and programs, since the service providers have a vested interest in ensuring that a project is actually implemented. Many of the country governments interviewed for the study also saw enormous potential in bundling, financing, and implementing energy efficiency projects on a larger scale in the public sector, a method that increases the rate of efficiency gains and creates further benefits through economies of scale.

The last fifteen years have seen Armenia emerge from Soviet rule and a severe economic and energy crisis, both complicated by its newfound political surroundings. The last ten years have seen significant reform and progress in the power sector which, when compared to the progress made by its neighbors, is all the more remarkable. The benefits of reform have not been easily won, however, and Armenia’s success is a tribute to its ability to learn from mistakes and persevere. A combination of improper planning and bad fortune forced the Government of Armenia to go through three separate tenders for its privatization assets. A combination of good planning and good fortune ultimately allowed for what has turned out to be one of the region’s most successful infrastructure privatizations so far.