Browsing by Author "Wu, Da-Chun"

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    Air Compressor Load Forecasting using Artificial Neural Network
    (Elsevier, 2021-04) Wu, Da-Chun; Bahrami Asl, Babak; Razban, Ali; Chen, Jie; Mechanical and Energy Engineering, School of Engineering and Technology
    Air compressor systems are responsible for approximately 10% of the electricity consumed in United States and European Union industry. As many researches have proven the effectiveness of using Artificial Neural Network in air compressor performance prediction, there is still a need to forecast the air compressor electrical load profile. The objective of this study is to predict compressed air systems' electrical load profile, which is valuable to industry practitioners as well as software providers in developing better practice and tools for load management and look-ahead scheduling programs. Two artificial neural networks, Two-Layer Feed-Forward Neural Network and Long Short-Term Memory were used to predict an air compressors electrical load. Compressors with three different control mechanisms are evaluated with a total number of 11,874 observations. The forecasts were validated using out-of-sample datasets with 5-fold cross-validation. Models produced average coefficient of determination values from 0.24 to 0.94, average root-mean-square errors from 0.05 kW - 5.83 kW, and mean absolute scaled errors from 0.20 to 1.33. The results indicate that both artificial neural networks yield good results for compressors using variable speed drive (average R2 = 0.8 and no naïve forecasting), only the long short-term memory model gives acceptable results for compressors using on/off control (average R2 = 0.82 and no naïve forecasting), and no satisfactory results are obtained for load/unload type air compressors (models constituting naïve forecasting).
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    ARC algorithm: A novel approach to forecast and manage daily electrical maximum demand
    (Elsevier, 2018-07) Wu, Da-Chun; Amini, Amin; Razban, Ali; Chen, Jie; Mechanical Engineering, School of Engineering and Technology
    This paper proposes an innovative algorithm for predicting short-term electrical maximum demand by using historical demand data. The ability to recognize in peak demand pattern for commercial or industrial customers would propose numerous direct and indirect benefits to the customers and utility providers in terms of demand reduction, cost control, and system stability. Prior works in electrical maximum demand forecasting have been mainly focused on seasonal effects, which is not a feasible approach for industrial manufacturing facilities in short-term load forecasting. The proposed algorithm, denoted as the Adaptive Rate of Change (ARC), determines the logarithmic rate-of-change in load profile prior to a peak by postulating the demand curve as a stochastic, mean-reverting process. The rationale behind this analysis, is that the energy efficient program requires not only demand estimation but also to warn the user of imminent maximum peak occurrence. This paper analyzes demand trend data and incorporates stochastic model and mean reverting half-life to develop an electrical maximum demand forecasting algorithm, which is statistically evaluated by cross-table and F-score for three different manufacturing facilities. The aggregate results show an overall accuracy of 0.91 and a F-score of 0.43, which indicates that the algorithm is effective predicting peak demand in predicting peak demand.
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    Data-driven Demand Control Ventilation Using Machine Learning CO2 Occupancy Detection Method
    (2020-07) Momeni, Mehdi; Wu, Da-Chun; Razban, Ali; Chen, Jie; Mechanical and Energy Engineering, School of Engineering and Technology
    Heating, ventilation, and air-conditioning (HVAC) system accounts for approximately 40% of total building energy consumption in the United States. Currently, most buildings still utilize constant air volume (CAV) systems with on/off control to meet the thermal loads. Such systems, without any consideration of occupancy, may ventilate a room excessively and result in a waste of energy. Previous studies show that CO2-based demand-controlled ventilation methods are the most widely used strategies to determine the optimal level of supply air volume. However, conventional CO2 mass balanced models do not yield an optimal estimation accuracy. In this manuscript, a data-driven control strategy was developed to optimize the energy consumption of supply fans by feed-forward neural network to predict real-time occupancy as an active constraint. As for the validation, the experiment was carried out in an auditorium located on a university campus. The result shows, after utilizing feed-forward neural network to enhance the occupancy estimation, the new primary fan schedule can reduce the daily ventilation energy by 75% when compared to the current on/off control.