This study investigates the performance of regression model, Kalman filter adaptation algorithm and artificial neural network to assess their qualities for predictions. It develops predictive algorithms based on price, temperature and humidity as multiple variables affecting time-varying aspect of electricity consumption. In order to meet energy demand through the use of electricity as an energy source for daily activities in buildings such as air conditioning, lighting, computers and cooking

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... oves., adequate allocation of energy resources and planning should be done, including predicting for electricity consumption. The process involves collecting data from the power grid of Faculty of Computer Science and Information Technology building, Universiti Malaysia Sarawak. The forecasting techniques were tested on the data collected, and the dataset consists of electricity consumption readings, with electricity price, humidity and temperature included in the forecasting model. The performances of regression model, artificial neural network and Kalman algorithm were tested using statistical evaluation parameters, root mean squared error (RMSE) and mean absolute percentage error (MAPE); while the parameter, standard deviation, was used to check the validity of models. This study identified Kalman algorithm as the most effective method of predicting consumption data compared to regression model, and artificial neural network.