Browsing by Author "Amini, Amin"

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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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    Hidden Wind Farms Potential for Residential Households Having Roofmounted Wind Arrester
    (IEEE, 2015) Amini, Amin; Kamoona, Mustafa; Department of Electrical and Computer Engineering, Purdue School of Engineering and Technology
    Small-scale energy-generating systems are being increasingly integrated into built environment, and the use of renewable energies is now spreading to old towns in developing countries. Despite the promise of free energy, the high-tech appearance of the harnessing tools of renewables has provoked criticism because of the incompatibility with the cultural/environmental characteristics of older towns in Iran. This paper presents a new concept of novel hidden wind farms in the residential households of Iranian desert-edge towns with roof-mounted wind-arresters. The results of this study show that a hidden wind farm integrated into old towns with the potential of tourism can eliminate the concern over the visibility and bird collisions as well as the use of land. In the present study, the old city of Ardakan, Yazd, with an arid climate located at the edge of a desert in the center of Iran, is selected as target case study. Calculations show that the application of one small-scale wind turbine per wind-arrester across the town can generate approximately 2.90 GWh a year. Moreover, the proposed concept could also be applied in other countries such as Afghanistan, Egypt, Pakistan, Iraq, UAE and some African countries.
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    A novel approach to forecast and manage electrical maximum demand
    (2017-06) Amini, Amin; Razban, Ali; Chen, Jie; Goodman, David
    Electric demand charge is a large portion (usually 40%) of electric bill in residential, commercial, and manufacturing sectors. This charge is based on the greatest of all demands that have occurred during a month recorded by utility provider for an end-user. During the past several years, electric demand forecasting have been broadly studied by utilities on account of the fact that it has a crucial impact on planning resources to provide consumers reliable power at all time; on the other hand, not many studies have been conducted on consumer side. In this thesis, a novel Maximum Daily Demand (MDD) forecasting method, called Adaptive-Rate-of-Change (ARC), is proposed by analysing real-time demand trend data and incorporating moving average calculations as well as rate of change formularization to develop a forecasting tool which can be applied on either utility or consumer sides. ARC algorithm is implemented on two different real case studies to develop very short-term load forecasting (VSTLF), short-term load forecasting (STLF), and medium-term load forecasting (MTLF). The Chi-square test is used to validate the forecasting results. The results of the test reveal that the ARC algorithm is 84% successful in forecasting maximum daily demands in a period of 72 days with the P-value equals to 0.0301. Demand charge is also estimated to be saved by $8,056 (345.6 kW) for the first year for case study I (a die casting company) by using ARC algorithm. Following that, a new Maximum Demand Management (MDM) method is proposed to provide electric consumers a complete package. The proposed MDM method broadens the electric consumer understanding of how MDD is sensitive to the temperature, production, occupancy, and different sub-systems. The MDM method are applied on two different real case studies to calculate sensitivities by using linear regression models. In all linear regression models, R-squareds calculated as 0.9037, 0.8987, and 0.8197 which indicate very good fits between fitted values and observed values. The results of proposed demand forecasting and management methods can be very helpful and beneficial in decision making for demand management and demand response program.