A Brief Review of Cuckoo Search Algorithm (CSA) Research Progression from 2010 to 2013

Cuckoo Search Algorithm is a new swarm intelligence algorithm which based on breeding behavior of the Cuckoo bird. This paper gives a brief insight of the advancement of the Cuckoo Search Algorithm from 2010 to 2013. The first half of this paper presents the publication trend of Cuckoo Search Algorithm. The remaining of this paper briefly explains the contribution of the individual publication related to Cuckoo Search Algorithm. It is believed that this paper will greatly benefit the reader who needs a bird-eyes view of the Cuckoo Search Algorithm’s publications trend

Electricity Consumption Forecasting in Thailand using Hybrid Model SARIMA and Gaussian Process with Combine Kernel Function Technique

Electricity consumption forecasting plays a significant role in planning electric systems. However, this can only be achieved if the demand is accurate estimation .This research, different forecasting methods hybrid SARIMA-ANN and hybrid model by SARIMA- Gaussian Processes with combine Kernel Function technique were utilized to formulate forecasting models of the electricity consumption . The objective was to compare the performance of two approaches and the empirical data used in this study was the historical data regarding the electricity consumption (gross domestic product: GDP, forecast values calculated by SARIMA model and electricity consumption) in Thailand from 2005 to 2015. New Kernel Function design techniques for forecasting under Gaussian processes are presented in sum and product formats. The results showed that the hybrid model by SARIMA - Gaussian Processes with combine Kernel Function technique outperformed the SARIMA-ANN model have the Mean absolute percentage error is 4.7072e-09, 4.8623 respectively. Keyword: Forecasting, Electricity Consumption, Model, Gaussian Process JEL Classifications: C13, C32, E27, P2

New methods and models for the ongoing commissioning of HVAC systems in commercial and institutional buildings

The performance of the HVAC systems in buildings tends to decrease after few years of operation. Equipment and sensors degradation lead to remarkable wastes of energy and money, as well as to the increase of building occupants thermal discomfort. HVAC ongoing commissioning (OCx), the continuation of HVAC commissioning well into the occupancy and operation phase of a building life, has been recognized as a cost-effective strategy to reduce energy wastes, equipment degradation and thermal discomfort. Building Automation Systems (BAS) collect and store huge amount of data for the purpose of building systems control. Those data represent a golden mine of information that can be used for the OCx of the building HVAC systems. This research work develops and validates new methods and models to be used for the OCx of HVAC systems using BAS measurements from commonly installed sensors. A Fault Detection and Identification (FD&I) method for chillers operation, and several virtual sensor models for variables of interest in Air Handling Units (AHUs) are presented. A FD&I method based on Principal Components Analysis (PCA) has been developed and used to detect abnormal operation conditions in an existing chiller operation and identify the responsible variables. The proposed FD&I method has been trained using measurements from summer 2009, and then used to detect abnormal observations from the following seven summer seasons (2010-2016). When the detected abnormal observations were replaced with artificially generated fault-free data, the proposed FD&I method did not detect any abnormal value along those artificially faulty-free variables. In summer 2016 the building operators changed several HVAC system operation set points, the FD&I method was effective in detecting almost 100% of the observations and properly identifying those variables whose set point was changed. For two different operation modes of an AHU several virtual outdoor air flow meters have been developed and the predictions have been compared against short-term measurements using uncertainty analysis and statistical indices. Three models have been investigated when the heat recovery coil was off. Results showed that the model with the simplest mathematical formulation was the most accurate, with the lowest value of uncertainty. When a heat recovery coil at the fresh air intake was on, two virtual flow meters have been developed to predict the outdoor air flow rate without the need of additional sensors. Both the models predicted the outdoor air ratio with good statistical indices: the Mean Absolute Error (MAE) was 0.015 for model a and 0.016 for model b. Three methods for the virtual measurement and/or calibration of air temperature and relative humidity have been developed for different AHU operation modes. These methods are different in terms of modelling strategy, information needed and technical knowledge required for implementation. For instance, results from the correction of the faulty measurements of the outdoor air temperature along a 24 hours period using Method A showed a high virtual calibration capability: MAE = 0.2°C and the Coefficient of Variation, CV-RMSE = 1.7%. A new definition of virtual sensor is proposed at the end of this research work. From a review of publications on virtual sensors for building application, the two most recurrent reason for the implementation of virtual sensor models (costs and practical issues) have been highlighted and integrated into the proposed new definition

Short-term forecasting of the electric demand of HVAC systems

Heating, Ventilation and Air Conditioning (HVAC) systems of large buildings have a high contribution to the electric grid peak demand. During those periods, electric utilities face important mismatch issues in power supply and demand. In the context of Demand Response (DR) programs, there is a need from building energy managers for tools to forecast the electric demand of HVAC systems to plan for fast-DR control strategies. This thesis contributes to the DR research field by proposing a method for multi-step forecasting of the electric demand of existing HVAC cooling systems on the short-term in large commercial and institutional buildings. Two forecasting methods are proposed: a cascade-based (global) method and a component-based method. The cascade-based method includes a sequence of forecasts of target variables. First, the air flow rate supplied by the AHUs is forecasted, followed by the cooling coils load, the cooling load of the whole building, and finally the electric demand of the primary cooling system is forecasted. The component-based method forecasts the electric demand of one component of the HVAC system such as a fan. Data-driven models are developed based on Building Automation System (BAS) trend data for electric demand forecasting of HVAC cooling system over the next six hours with a time-step of 15 minutes. The large amount of data collected through the BAS presents a gold mine of information which could be used for better understanding of the actual building operation and performance. Data mining techniques are used as pre-processing steps to help in the development of the forecasting models, for the selection of regressors, to identify typical daily profiles of the target variable and to better understand the operation of HVAC systems. Different sequences of preprocessing steps are tested and their impact on the forecasting performance is compared. The proposed forecasting methods are validated on two case studies: the Genomic research center on Loyola Campus of Concordia University and an office building located in Shawinigan-Sud (Québec). The thesis compares the forecasts with measurements, and discusses the quality of forecasting results