Improved Methods to Load Prediction in Commercial Buildings

Accurate load prediction methods in commercial buildings can provide a benefit to understanding the energy behavior of commercial buildings for improved building energy management. A crucial first step to understanding the potential energy savings and to developing a proper building energy management system in commercial buildings is the development of a proper baseline estimate that can be used as an accurate gage of future real savings in commercial buildings. Without an accurate method, load predictions and calculated energy savings can vary from 5-10% with even larger error on days when unusual occurrences may cause major fluctuations in load behavior. We introduce the use of fuzzy logic to improve the load prediction potential for commercial buildings. The method is then compared to an existing baseline calculation commonly used for predicting commercial building electrical loads. The method proposed involves the development of membership functions using various inputs, including real-time, morning-of, and previous ten-day data. The algorithm uses Modified Learning from Experience (MLFE) to generate membership functions using a training set of chosen inputs. These membership functions are then improved with a Recursive Least Squares (RLS) method that determines a single output power prediction within a given tolerance set by the user, for a given time. Improvements, using this method, are seen in the robustness of the predictions to abnormal circumstances affecting inputs, thus allowing minimization of plant uncertainty, improvement of stability and performance for building control systems used for load shedding, and energy efficiency improvements. Introduction to Current Methods The central aim of this paper is to review current methods for predicting hourly power use and to develop an alternative method that provides a level of accuracy comparable or better than current methods. An additional goal is to design a method that is easily implementable in real world automated building management systems during demand response events. For clarity, Demand Response (DR) can be defined as actions taken to reduce electric loads when contingencies, such as emergencies and congestion, occur that threaten supply-demand balance, or market conditions raise supply costs. A number of methods have been developed in the interest of accurately predicting commercial building electrical loads for issues stemming from the need to gage building energy efficiency or demand response strategy effectiveness. A number of authors have contributed new insights to prediction models for commercial building power consumption. These types of models include seasonal regression, simple average, Fourier series, and artificial neural network models. The use of fuzzy logic has become widespread in many applications in which systems are uncertain, or non-linear, including short-term load forecasting for transmission networks, yet the use of fuzzy logic for the prediction of hourly building electrical load has not yet been examined or published. Researchers at Lawrence Berkeley National Laboratory have developed seasonal and 10day regression models as well as simple three or ten day averages with morning correction factors. In their study, seven load prediction methods are developed and tested on 32 commercial buildings. Accuracy and bias are compared for each of the models (Coughlin et al. 2008). Other models of predicting commercial building load have used Fourier series methods that utilize the periodic behavior found in commercial buildings. These methods utilize seasonal, weather, and hourly parameters such as ambient temperature, humidity and solar radiation to develop a general linear regression modeling procedure at the hourly level. These methods were tested at Zachary Engineering Center and accurate results were produced (Dhar, Reddy & Claridge 1993). A number of publications have focused on the use of artificial neural network (ANN) models in accurately forecasting load. These models mimic the learning process of the human brain in learning relationships that may exist between input parameters and controlled and uncontrolled variables (Kalogirou 1998). Artificial neural networks function as adaptive systems that change their structure based on external or internal information flowing through the network during the learning phase (Quaiyum et al. 2011). There are many different types of ANN models, but they can be classified into three main categories; feed-forward, feedback, and autoassociative methods. Feedforward neural networks are arguably the simplest type of ANNs and are used commonly in predicting uncertain outcomes. Information moves only in one direction, from the input nodes through parallel hidden nodes and then on to the output nodes. In feed-forward ANN models, a learning rate, the number of hidden nodes in the ANN, and the number of epochs are specified, and the predicted output of the model is given in equations resembling the following form , where is the output of the ANN model (Ortiz-Arroyo, Skov and Huynh 2005). Feedback artificial neural networks (FB ANN), or recurrent neural networks, contain information that travels in both directions. The internal state of the network can exhibit dynamic temporal behavior unlike feedforward neural networks (Graves et al. 2009). Feedback neural networks have been examined for building load prediction by Spanish researchers P. Gonzales and J. Zamarreno. The process they use takes input temperatures (used in a temperature predictor for temperature forecasting), time and past power data. The FB ANN does not allow the possibility of system interruption for stability so it includes non-characteristic or abnormal days in its prediction method. The model includes feedback of the actual output variable for the past input variables and predicts the next value depending on the quality of the “acquired knowledge” of the system. This system was compared to other ANN methods and performed much better than feed-forward, auto-associative, and non-linear Bayesian regression methods (Gonzales & Zamarreno 2005). Fuzzy methods have been implemented in a number of areas over the past thirty years. A common application of fuzzy methods is in short-term load forecasting for stability of the electrical grid. Short term load forecasting is especially significant for economic load dispatch, load management scheduling, optimum power flow with minimum transmission loss, fuel management, and contingency planning (Sachdeva & Verma 2008; Pandian et al. 2005). Through these studies with short term forecasting, fuzzy methods have been proven capable of being applied to uncertain systems effectively and thus these methods can be extended to determining power consumption in buildings. Current Methods Chosen for Comparison As mentioned in the previous section, there are a multitude of methods that exist in the area of load prediction, yet, many of these methods are highly complex or require large amounts of computational software to be completed. One of the main goals of the method to be developed is easy implementation, meaning it is low cost and requires minimal computation time, which allows us to meet the goal of effective reference-tracking feedback control. The method selected for comparison requires similar computation times and costs. We compared our method to LBNL’s BLP3 method due to the fact that it has performed well and requires little computation and no dependence on special software for implementation.