Load prediction with an improved feature selection method for building energy management of an office park

Load prediction plays a significant role in building energy management. An accurate HVAC load prediction model highly depends on the feature selection and the quality of training data. In previous work on load prediction, the input features are majorly manually selected by expertise, which is relatively subjective and lacks theoretical supports. Using the real building operational data collected from an office park located in Hangzhou, this paper developed a short-term cooling load prediction model, in which the input features are selected based on an analysis on the heat transfer process. Combined with qualitative analysis of the real data, several features such as outdoor air enthalpy and indoor black-bulb temperatures from different orientations are introduced into the model. The proposed model was then applied to the HVAC control system of the office park. Compared to the load prediction model with commonly used features, the proposed model reduced CRVMSE by 21% and MAPE by 30% during the operation period of the system. Furthermore, the impacts of training dataset size and prediction time range on model’s accuracy and training time were discussed

A Bidding Model for a Virtual Power Plant via Robust Optimization Approach

The evolution of the energy markets has been accelerating the use of distributed energy resources (DERs) all over the world. Virtual power plant (VPP) is a new method to management this increasing two-way complexity. In this paper, a bidding model for a VPP via robust optimization in the uncertain environment of the electricity market is presented. The flexible feature embedded in the model with respect to solution accuracy and computation burden would be advantageous to the VPP. Results of a case study are provided to show the applicability of the proposed bidding model

A Bidding Model for a Virtual Power Plant via Robust Optimization Approach

The evolution of the energy markets has been accelerating the use of distributed energy resources (DERs) all over the world. Virtual power plant (VPP) is a new method to management this increasing two-way complexity. In this paper, a bidding model for a VPP via robust optimization in the uncertain environment of the electricity market is presented. The flexible feature embedded in the model with respect to solution accuracy and computation burden would be advantageous to the VPP. Results of a case study are provided to show the applicability of the proposed bidding model

Isothermal and isovolumetric process of CO2 adsorption on nitrogen-doped biochar: Equilibrium and non-equilibrium states

In order to reveal the thermodynamic properties of CO2 adsorption on a promising nitrogen-doped biochar under isothermal and isovolumetric conditions, the adsorption isotherms based on 391 data points were experimentally obtained to investigate the mass and energy transfer process during CO2 adsorption by combining with model analysis. Then a series of interesting phenomena were found by analyzing those thermodynamic parameters in the equilibrium and non-equilibrium states. The capacity of CO2 on the biochar non-linearly increases with an increase of the initial pressure and volume-mass ratio but the decrease of the adsorption temperature. It can be up to 7.6 mol/kg at 273 K and 100 kPa. The adsorption system exchanges the energy with the surrounding environment mainly by heat transfer. And the interfacial energy of the adsorbent can be affected by the adsorbate system in three parts: pressure change from gas phase, molecular force from adsorbed phase and heat transfer. Then the conditions with low adsorption temperature, high initial pressure and large volume-mass ratio can provide a strong driving force for CO2 adsorption. These phenomena that have not been reported before will help us get a better technical process for CO2 capture

Analysis of Thrust-Scaled Acoustic Emissions of Aircraft Propellers and Their Dependence on Propulsive Efficiency

The increasing demand for short-range passenger air transport and the strong push for aircraft with electric propulsion has renewed research interest in propellers. Despite the unmatched aerodynamic efficiency of propellers, their relatively high noise emissions limit widespread application on aircraft. Previous research has not systematically addressed the tradeoff between aerodynamic and aeroacoustic performance. This paper presents the results of an optimization study aimed at minimizing propeller noise without compromising aerodynamic efficiency. In the optimization, a blade-element-momentum-theory (BEMT) model is utilized which accounts for the effects of blade sweep on the blade loading. This BEMT model is coupled to a frequency-domain code for tonal noise prediction. A novel scaling approach is presented to directly relate the propeller noise emissions to the propeller thrust. Dedicated wind-tunnel experiments were performed to validate the analysis models. Good agreement between numerical and experimental results is obtained at low to moderate blade loading conditions. The optimization study shows that the blade sweep is an important design parameter to simultaneously maximize aerodynamic and acoustic performance. Compared to a modern baseline design, a noise reduction of 2.9 dB is achieved without reduction in propeller efficiency.Flight Performance and Propulsio