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The importance of LCEM (life cycle energy management) has been recognized from the view of life cycle energy savings for sustainable buildings. The purposes of this research are the proposal of an LCEM framework and development of prototype HVAC system simulation tools for LCEM. In this paper, the necessity of energy simulation tools for LCEM is discussed, and the outline and solution method of the simulation tool are shown.

A brisk building boom of hydropower mega-dams is underway from China to Brazil. Whether benefits of new dams will outweigh costs remains unresolved despite contentious debates. We investigate this question with the "outside view" or "reference class forecasting" based on literature on decision-making under uncertainty in psychology. We find overwhelming evidence that budgets are systematically biased below actual costs of large hydropower dams - excluding inflation, substantial debt servicing, environmental, and social costs. Using the largest and most reliable reference data of its kind and multilevel statistical techniques applied to large dams for the first time, we were successful in fitting parsimonious models to predict cost and schedule overruns. The outside view suggests that in most countries large hydropower dams will be too costly in absolute terms and take too long to build to deliver a positive risk-adjusted return unless suitable risk management measures outlined in this paper can be affordably provided. Policymakers, particularly in developing countries, are advised to prefer agile energy alternatives that can be built over shorter time horizons to energy megaprojects.

AbstractThe future of energy mobility involves networks of users, operators, organizations, vehicles, charging stations, communications, materials, transportation corridors, points of service, and so on. The integration of smart grids with plug‐in electric vehicle technologies has societal and commercial advantages that include improving grid stability, minimizing dependence on nonrenewable fuels, reducing vehicle emissions, and reducing the cost of electric vehicle ownership. However, ineffective or delayed participation of particular groups of stakeholders could disrupt industry plans and delay the desired outcomes. This article develops a framework to address enterprise resilience for two modes of disruptions—the first being the influence of scenarios on priorities and the second being the influence of multiple groups of stakeholders on priorities. The innovation of this study is to obtain the advantages of integrating two recent approaches: scenario‐based preferences modeling and stakeholder mapping. Public agencies, grid operators, plug‐in electric vehicle owners, and vehicle manufacturers are the four groups of stakeholders that are considered in this framework, along with the influence of four scenarios on priorities.

AbstractA parametric analysis for an innovative prototype of passive building, located in south Italy and for residential use, has been conducted to evaluate the thermal energy requirements for heating and cooling applications. The investigation was addressed by considering also the aspect of sustainability, by employing natural materials such as dry sand and wood fibre, and the correspondent effects on the energy performances of the envelope. These materials are usually available on site; they increase the building thermal capacity, which represents a crucial aspect for hot climates, and finally could even be reused after building disposal. The construction system based on the completely dry assembling technique makes the exploitation of the mentioned materials possible. The results of the parametric study were obtained by means of the Design Builder dynamic software, by investigating the glazed surfaces, the control of solar radiation and the exploitation of nocturnal free-cooling. A parametric study allowed for optimization of the envelope, by respecting the limit values of 15 kWh/m2 suggested by the standard passivhaus in its extended formulation for warm climates.

Energy Certificates of Buildings (ECB) provide interesting information on the standard energy performance, thermo-physical and geometrical related properties of existing buildings. The analysis of such data collection is challenging due to data volume and heterogeneity of attributes. This paper presents EPICA a data mining framework to automatically explore a collection of ECB to extract interesting knowledge items. To this aim, EPICA first reduces the data dimensionality through the Principal Component Analysis, then a clustering algorithm is exploited to discover groups of ECB with similar features. Each group is then locally characterized by a set of relevant generalized association rules able to summarize interesting relations among variables influencing energy performance of buildings at different coarse granularities. Experimental results, obtained on real data collected from an energy certification dataset related to Piedmont Region, in North Western of Italy, shows the effectiveness of EPICA in extracting a manageable set of human-readable knowledge items characterizing the groups of buildings with different energy performance levels.

This research investigates the relationships among renewable energy consumption, economic growth, and financial development in five sub-Saharan African nations utilizing panel data from 2000 to 2020. Econometric methods are used to ascertain the existence or absence of cross-sectional dependence and the short-run and long-run connections between the following factors: Pesaran cross-sectional dependence (CD) and cross-sectionally augmented IPS (CIPS) unit root tests, pooled mean group (PMG), and dynamic ordinary least squares (DOLS) estimations. The presence of cross-sectional dependence is found and represented with the CIPS unit root test. No significant short-run relationship is found between the variables of the study, yet a significant long-run relationship is present among them. A positive relationship exists between CO2 emissions and financial development, while financial development and renewable energy consumption are found to have negative relationships with CO2 emissions. The study also supports the scale effect of the environmental Kuznets curve hypothesis. Additionally, no causality is found among the variables, and impulse response and variance decomposition estimation are carried out to recommend future effects. Policy implications of findings are discussed, with accompanying suggestions.

Abstract In recent years, modern appliances with high electricity demand have played a significant role in residential energy consumption. Despite the positive impact of these appliances on the quality of life, they suffer from major drawbacks, such as serious environmental concerns and high electricity bills. This paper introduces a consolidated framework of load management to alleviate those drawbacks. Initially, benefiting from a demonstrative analysis of home energy consumption data, controllable and responsive appliances in smart home are identified. Then, the energy consumption pattern is reduced and shifted using flexible load models and better utilization of existing energy storage systems. This can be achieved through data mining approaches, i.e., density-based spatial clustering of application with noise (DBSCAN) method. In this technique, no sensor for detection or measurement instruments will be required, whose deployment incur cost to the system or increase security risk for consumers. In the following, one scheduling of using controllable appliances, which is formulated by convex optimization, is considered for the demand response (DR) program, provided that this plan doesn't affect customers’ priority and convenience. In the last stage, the deployment of energy storage systems, such as plug-in hybrid electric vehicles (PHEVs) and battery energy storage systems (BESS), is introduced to lower the energy cost and improve the performance of the proposed DR model. Simulation results of this demand response are compared with conventional k-clustering methods to confirm the economic superiority of the DBSCAN clustering technique using the data of a residential unit during three different scenarios.

A balance between industrial pollution and economic growth becomes a major policy issue to attain a sustainable society in the world. To discuss the problem from economics and business perspectives, this study proposes a new use of DEA (Data Envelopment Analysis) as a methodology for unified (operational and environmental) assessment. A unique feature of the proposed approach is that it separates outputs into desirable and undesirable categories. Such separation is important because energy industries usually produce both desirable and undesirable outputs. This study discusses how to unify the two types of outputs under natural and managerial disposability. The proposed DEA approach evaluates various organizations by the three efficiency measures such as OE (Operational Efficiency), UEN (Unified Efficiency under Natural disposability) and UENM (Unified Efficiency under Natural and Managerial disposability). An important feature of UENM is that it separates inputs into two categories and unifies them under the two disposability concepts in addition to the proposed output separation and unification. This study incorporates an amount of capital assets for technology innovation, as one of the two input group, into the measurement of UENM. Then, it compares UENM with the other two efficiency measures. This study is the first research effort in which DEA has an analytical capability to quantify the importance of investment on capital assets for technology innovation. To confirm the practicality of the proposed approach, this study applies the three efficiency measures to a data set regarding manufacturing and non-manufacturing industries of 47 prefectures in Japan. This study empirically confirms the validity of Porter hypothesis in Japanese manufacturing industries, so implying that environmental regulation has been effective for betterment on the performance of Japanese manufacturing industries. Another important finding is that the emission of greenhouse gases is a main source of unified inefficiency in the two groups of industries. Therefore, Japanese industries, examined in this study, need to make their efforts to reduce the greenhouse gas emissions and air pollution substances by investing in capital assets for technology innovation.