This paper proposes Artificial Neural Network (ANN) to determine adjusted baseline energy for quantifying energy savings from an energy efficiency program implemented in an office building. The input data to the ANN includes number of working days and cooling degree days (CDD) each month for one year period before implementation of the retrofitting program. On the other hand, output data is baseline energy use (i.e. energy use before retrofit). Since the input data to the network encompasses of 36 months set of data only, Bootstrap method is used to generate more input data without changing the input and output trend of the original data set. This is performed to increase validity of the training process. Once the optimum training parameters have been obtained, adjusted baseline energy is determined by feeding the number of working days and CDDs in the post-retrofit period (i.e. 12 months set of data) to the network. Energy savings is then calculated by comparing the adjusted baseline energy with the energy use after implementing the retrofit program. The performances of the ANN model are then compared with Multi-regression technique in term of R2, Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE) and Mean Absolute Deviation (MAD). Results show that the proposed ANN model has smaller errors and R2 closer to one compare to Multi-regression technique.

Power quality issues has evidently become one of the crucial issues in power system studies. With the integration of distributed generations (DGs) in the power system, the issue of voltage control has become very important as the power system has become dynamic due to the bidirectional power flow. Delivering power while maintaining acceptable voltage limits has become very challenging for the distribution network operators (DNOs). In order to maintain power delivery to the customers whilst maintaining good power quality, the DNOs has undertaken several measures including implementing several voltage control methods to guarantee that the voltage limits in the power system are within permissible limits. This includes the centralized and decentralized voltage control techniques in the system connected with DGs. The DGs are connected to buses 9 and 14 of the test system where these two buses have been identified as the weakest bus. The decentralized voltage control method has also become an acceptable option due to its ease in having limited communication and lower costs in the system. One of the decentralized voltage control method which has been widely used is the power factor control (PFC) method. The IEEE 14 bus distribution test system has been used and integrated with DGs to show the effectiveness of the PFC method in managing the voltage fluctuation issues in the distribution system.