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Abstract Building energy consumption is vulnerable to climate change due to the direct relationship between outside temperature and space cooling/heating. This work quantifies how the relationship between climate change and building energy consumption varies across a range of building types at different spatiotemporal scales based on estimates in 925 U.S. locations. Large increases in building energy consumption are found in the summer (e.g., 39% increase in August for the secondary school building), especially during the daytime (e.g., >100% increase for the warehouse building, 5–6 p.m.), while decreases are found in the winter. At the spatial scale of climate-zones, annual energy consumption changes range from −17% to +21%, while at the local scale, changes range from −20% to +24%. Buildings in the warm-humid (Southeast) climate zones show larger changes than those in other regions. The variation of impact within climate zones can be larger than the variation between climate zones, suggesting a potential bias when estimating climate-zone scale changes with a small number of representative locations. The large variations found in the relationship between climate change and building energy consumption highlight the importance of assessing climate change impacts at local scales, and the need for adaptation/mitigation strategies tailored to different building types.

Load forecasting has long been recognized as an important building block for all utility operational planning efforts. Over the recent years, it has become ever more challenging to make accurate forecasts due to the proliferation of distributed energy resources, despite the abundance of existing load forecasting methods. In this paper, we identify one drawback suffered by most load forecasting methods: neglect to thoroughly address the impact of input errors on load forecasts. As a potential solution, we propose to incorporate input modeling and uncertainty quantification to improve load forecasting performance via a two-stage approach. The proposed two-stage approach has the following merits. (1) It provides input modeling and quantifies the impact of input errors, rather than neglecting or mitigating the impact, a prevalent practice of existing methods. (2) It propagates the impact of input errors into the ultimate point and interval predictions for the target customer's load to improve predictive performance. (3) A variance-based global sensitivity analysis method is further proposed for input-space dimensionality reduction in both stages to enhance the computational efficiency. Numerical experiments show that the proposed two-stage approach outperforms competing load forecasting methods in terms of both point predictive accuracy and coverage ability of the predictive intervals. 9 pages, 4 figures, journal

Abstract Average energy consumption per U.S. household has fallen by just under 20% in the last ten years. Much of this drop occurred after 1979, when gas and electricity prices as well as oil prices rose in real terms. The response of households to higher prices has involved physical modifications on and in the home and changes in behavior. Many actions have been taken by households, but the most important single factor has been a significant reduction in indoor temperatures. The greater energy efficiency of new homes and appliances has also helped to depress residential energy demand, although improvements have levelled off in the last few years. There are signs that the momentum of energy conservation is less now than it was 2 years ago, but it appears that energy prices will be high enough to discourage households from returning to former energy-using practices. Along with the continued replacement of homes and appliances with more efficient models, and other factors such as the migration to wanner regions and the movement to more apartments and smaller homes, this will probably keep U.S. residential energy consumption at about its present level through the 1980s.

Abstract In energy demand forecasting, the objective function is often symmetric, implying that over-prediction errors and under-prediction errors have the same consequences. In practice, these two types of errors generally incur very different costs. To accommodate this, we propose a machine learning algorithm with a cost-oriented asymmetric loss function in the training procedure. Specifically, we develop a new support vector regression incorporating a linear-linear cost function and the insensitivity parameter for sufficient fitting. The electric load data from the state of New South Wales in Australia is used to show the superiority of our proposed framework. Compared with the basic support vector regression, our new asymmetric support vector regression framework for multi-step load forecasting results in a daily economic cost reduction ranging from 42.19 % to 57.39 % , depending on the actual cost ratio of the two types of errors.

The intermittent property of a photovoltaic (PV) system requires supplementary energy such as the utility grid or batteries to meet load demand. However, when large scale PV systems are connected to the utility grid, they might affect the grid stability if the overall system is not properly designed. Hence, an accurate model for forecasting the PV system output would be useful in enhancing the system stability and reliability. The dynamic modelling of PV systems is thus crucial to the rapidly developing technologies and integrated sources in the smart grid application. This paper presents different approaches to model PV systems and identifies their pros and cons in modelling. The paper then explains the importance of a dynamic model, followed by the methodology in building up such a dynamic model. A three-vertex representation of a nearby building casting a shadow onto the PV array is also proposed as a novel approach in shadow analysis. The implementation of the dynamic model for PV systems was demonstrated in a case study in Hong Kong.

An operational cost minimisation model is established for a smart energy hub (S.E. Hub) consisting of a combined heat and power (CHP) unit, a heating, ventilation and air-conditioning (HVAC) system, and thermal and electricity storage units. The optimal operation of CHP is combined with the load management of HVAC under a time-of-use (TOU) tariff. The heat and power split ratio of CHP is dynamically determined during the operation. The scheduling of HVAC load and the charging/discharging of energy storage systems are also determined through the optimisation model. The energy management system can therefore shift the load demand and manage energy supply simultaneously. System operation requirements and environment factors including the outdoor air-temperature variation, seasonal variation, and battery degradation are considered. Comprehensive case studies are carried out to examine the effectiveness of the proposed strategy, from which insights are obtained for different energy management strategies and possible upgrade of S.E. Hub. Simulation results reveal that dynamic control of the CHP heat and power split ratio is an effective way to save the total operational cost, and a clear cost saving is shown through the proposed optimal operation strategy.

Abstract The feasibility of an alternative CO 2 mitigation system and a methanol production process is investigated. The Carnol system has three components: (i) a coal-fired power plant supplying flue gas CO 2 , (ii) a process which converts the CO 2 in the presence of He from natural gas to methanol, (iii) use of methanol as a fuel component in the automotive sector. For the methanol production process alone, up to 100% CO 2 emission reduction can be achieved; for the entire system, up to 65% CO 2 emission reduction can be obtained. The Carnol system is technically feasible and economically competitive with alternative CO 2 -disposal systems for coal-fired power plants. The Carnol process is estimated to be economically attractive compared to the current market price of methanol, especially if credit can be taken for carbon as a marketable coproduct.

Abstract The mutual oxidation of n-pentane and NO2 at 500–1000 K has been studied at equivalence ratios of 0.5 and 1.33 by using an atmospheric-pressure jet stirred reactor (JSR). N-pentane, O2, NO, NO2, CO, CO2, CH2O, C2H4, and CH3CHO are simultaneously quantified, in-situ by using an electron-impact molecular beam mass spectrometer (EI-MBMS), a micro-gas chromatograph (μ-GC), and a mid-IR dual-modulation faraday rotation spectrometer (DM-FRS). Both fuel lean and rich experiments show that, in 550–650 K, NO2 addition inhibits low temperature oxidation. With an increase of temperature to the negative temperature coefficient (NTC) region (650–750 K), NO2 addition weakens the NTC behavior. In 750–1000 K, high temperature oxidation is accelerated with NO2 addition and shifted to lower temperature. Two kinetic models, a newly developed RMG n-pentane/NOx model and Zhao's n-pentane/NOx model (Zhao et al., 2018, Submitted) were validated against experimental data. Both models were able to capture the temperature-dependent NO2 sensitization characteristics successfully. The results show that although NO2 addition in n-pentane has similar effects to NO at many conditions due to fast NO and NO2 interconversion at higher temperature, it affects low temperature oxidation somewhat differently. When NO2/NO interconversion is slow, NO2 is relatively inert while NO can strongly promote or inhibit oxidation.