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In some statistical process control (SPC) applications, it is assumed that a quality characteristic or a vector of quality characteristics of interest follows a univariate or multivariate normal distribution, respectively. However, in certain applications this assumption may fail to hold and could lead to misleading results. In this paper, we study the effect of non-normality when the quality of a process or product is characterized by a linear profile. Skewed and heavy-tailed symmetric non-normal distributions are used to evaluate the non-normality effect numerically. The results reveal that the method proposed by Kim et al. (J. Qual. Technol. 2003; 35:317-328) can be designed to be robust to non-normality for both highly skewed and heavy-tailed distributions.

h i g h l i g h t sWe propose a model for forecasting cooling and electricity load demand. The model takes the advantage of both time series and regression methods. The model is able to accurately forecast the load demands of the CCHP system. a r t i c l e i n f o t r a c tThe objective of this paper is to extend a statistical approach to effectively provide look-ahead forecasts for cooling and electricity demand load. Our proposed model is a generalized form of a Cochrane-Orcutt estimation technique that combines a multiple linear regression model and a seasonal autoregressive moving average model. The proposed model is adaptive so that it updates forecast values every time that new information on cooling and electricity load is received. Therefore, the model can simultaneously take advantage of two statistical methods, time series, and linear regression in an adaptive way. The effectiveness of the proposed forecast model is shown through a use case. The example utilizes the proposed approach for economic dispatching of a combined cooling, heating and power (CCHP) plant at the University of California, Irvine. The results reveal the effectiveness of the proposed forecast model.

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