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Forecasting Cooling Energy in Real-time and Predicting Impact of Cooling Set-Point Change on Demand ReductionAuthor: Lingamallu Sree Manasa Date: 2019-06-06 Report no: IIIT/TH/2019/54 Advisor:Vishal Garg AbstractForecasting energy consumption enables users to plan their resource utilization optimally. For this,it is essential to establish a reliable forecast of consumption profile in real-time, which is very challenging and still emerging. In this study, we focus on forecasting HVAC cooling energy consumption at a sub-hourly forecasting horizon which enables us to analyze and control the demand in real-time. With the use of energy meters and BACnet technology in the target building (KCIS), we have collected the cooling energy consumption data and HVAC systems usage at a higher resolution in the target building. In this methodology, weather data from Weather Underground of Gachibowli, Hyderabad location has been used. These recorded weather parameters are being used as explanatory variables. Along with the weather parameters, many other feature-engineered explanatory variables which are discussed in the thesis have been used to capture the trend and variation of energy consumption. Required pre-processing and data analysis is performed on the raw data to obtain meaningful information for modeling. We have implemented a one-step-ahead static forecasting using regression and deep learning techniques like Lin- ear Regression, Support Vector Machine (SVM) Regression, K-Nearest Neighbour (KNN) Regression, Gradient Boosting Regression Trees (GBRT), Long Short-Term Memory (LSTM). By comparing the results obtained from using the aforementioned forecast learning algorithms for various combinations of input parameters/explanatory variables, we have carried out a critical model evaluation to attain an ex-emplary forecasting model. All the methods were observed to fit comparatively well. However, GBRT was observed to provide better results marginally and consistently. Our results show that we could improve forecast accuracy by almost 45% by including feature-engineered variables. We have achieved a Mean Absolute Percentage Error (MAPE) of 14.7% with GBRT for the best combination of input features. We observe that at higher values of energy consumption, we are able to forecast with higher accuracy. For energy values greater than 10kWh, the GBRT model was observed to produce precise results with an accuracy of 9.6% MAPE. The model evaluation was done for a data-set ranging from the months of April to August in 2018, as the data of the real building is available during this period. We further evaluate the performance of the model throughout the year based on the data generated from the EnergyPlus simulation software. As calibrating the target building is an onerous and massive task, we design a basic model with realistic set-point, occupancy and internal load schedules. We have achieved a MAPE of 6.7% for the simulated data. Based on recent studies in peak demand reduction, simple measures like increasing cooling set-point temperatures in the case of HVAC systems load management, serves as an effective demand response. Majority of the past studies in demand response focus on developing strategies that reduce peak demand. To practically adapt these strategies, there is a necessity that the user is capable of understanding the impact of these strategies and aware of how to respond, especially during peak demand. Research in estimating the potential of DR programs is gaining momentum these days and a lot of research has to be done to develop reliable applications that can be used in real-time. In the next part of the thesis, we have therefore focused on developing a model that predicts the impact of changing the HVAC set-point temperatures on the cooling energy demand. The model was thus evaluated on the basis of the afore-mentioned simulated data. The model performance was evaluated by changing set-point temperatures for a period of time and comparing them with the results obtained from simulation. An analysis was performed to test the model performance at any given time in a day. We have achieved a MAPE of 7%. In the next part of the thesis, we have demonstrated the application of the prediction model for the demand response. This is a simple and user friendly application using which the decision makers or the building energy engineers can know how to respond during a peak demand. After a DR event when the set-points are back to the normal schedule, we have observed peaks while the energy is ramping up. For buildings which have a prescribed demand limit, these peaks cause demand penalty. Addressing this issue, we propose a strategy to enable stable ramping up process Full thesis: pdf Centre for IT in Building Science |
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