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Energy efficiency applications have great importance for facilities that utilize large amount of electrical and heat energy. Cogeneration (so called Combined Heat and Power; abbreviated as CHP) plants with gas engines are capable of generating both electrical and heat energy simultaneously using a single fuel input. In recent years, the realization of license exemption for facilities willing to produce electricity just for their energy demands by ensuring the condition of 80% total efficiency, low carbon emission of systems contain gas engines, rapid operation for synchronization and shortness of payback periods make cogeneration and trigeneration (so called Combined Cooling, Heat and Power; abbreviated as CCHP) plants more popular. This paper (i) briefly reviews cogeneration and trigeneration plants and their advantages, (ii) presents a novel methodology to determine the optimal capacity ratings for the plants by using the energy consumption profile, (iii) illustrates the calculation procedures including economic profit, thermal efficiency, and electricity generation of the selected system, and (iv) suggests the optimal capacity, plant placement and configuration for a medium-scale hospital. The energy savings potential at the university hospital is estimated as 19.66% and 19.52% with the use of natural gas based cogeneration and trigeneration plant, respectively. V C 2015 AIP Publishing LLC. [http://dx.

Over the past decade, energy forecasting applications not only on the grid side of electric power systems but also on the customer side for load and demand prediction purposes have become ubiquitous after the advancements in the smart grid technologies. Within this context, short-term electrical energy consumption forecasting is a requisite for energy management and planning of all buildings from households and residences in the small-scale to huge building complexes in the large-scale. Today’s popular machine learning algorithms in the literature are commonly used to forecast short-term building electrical energy consumption by generating an abstruse analytical expression between explanatory variables and response variables. In this study, gene expression programming (GEP) and group method of data handling (GMDH) networks are meticulously employed for creating genuine and easily understandable mathematical models among predictor variables and target variables and forecasting short-term electrical energy consumption, belonging to a large hospital complex situated in the Eastern Mediterranean. Consequently, acquired results yielded mean absolute percentage errors of 0.620% for GMDH networks and 0.641% for GEP models, which reveal that the forecasting process can be accomplished and formulated simultaneously via proposed algorithms without the need of applying feature selection methods.

Electrical energy forecasting is crucial for efficient, reliable, and economic operations of hospitals due to serving 365 days a year, 24/7, and they require round-the-clock energy. An accurate prediction of energy consumption is particularly required for energy management, maintenance scheduling, and future renewable investment planning of large facilities. The main objective of this study is to forecast electrical energy demand by performing and comparing well-known techniques, which are frequently applied to short-term electrical energy forecasting problem in the literature, such as multiple linear regression as a statistical technique and artificial intelligence techniques including artificial neural networks containing multilayer perceptron neural networks and radial basis function networks, and support vector machines through a case study of a regional hospital in the medium-term horizon. In this study, a state-of-the-art literature review of medium-term electrical energy forecasting, data set information, fundamentals of statistical and artificial intelligence techniques, analyses for aforementioned methodologies, and the obtained results are described meticulously. Consequently, support vector machines model with a Gaussian kernel has the best validation performance, and the study revealed that seasonality has a dominant influence on forecasting performance. Hence heating, ventilation, and air-conditioning systems cover