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- Short-term load forecasting using information obtained from low voltage load profilesPublication . Sousa, João; Pires Neves, Luís; Humberto M.M. JorgeRecent researches in load forecasting are quite often based on the use of neural networks in order to predict a specific variable (maximum demand, active electric power or hourly consumption) using past values of the same variable and other exogenous factors proved to influence the value being predicted. This work aims to explore different input patterns in neural networks incorporating information derived from load profiles of different consumers' classes.
- Load forecasting based on neural networks and load profilingPublication . Sousa, João; Pires Neves, Luís; H. M. JorgeThis work presents a novel perspective of load forecasting based on neural networks and load profiling. In addition to the variables that are typically used to predict future load demand, such as past load values, meteorological variables, seasonal effects or macroeconomic indexes, it is expected that the use of load profiles and detailed information of individual consumers could favor the forecasting process. The methodology can be extended to different temporal horizons being predicted and the eventual threat of overparametrization is attenuated by the use of neural networks since the complexity of the model does not necessarily depends on the number of its weights and biases, as some of these parameters might be found irrelevant in the process. Another way to reduce the risk of overparametrization and overfitting is through the use of a considerable number of data points (whenever historical data is available) to train the network.
- Forecasting Heating and Cooling Energy Demand in an Office Building using Machine Learning MethodsPublication . Godinho, Xavier; Bernardo, Hermano; Oliveira, Filipe Tadeu; Sousa, JoãoForecasting heating and cooling energy demand in buildings plays a critical role in supporting building management and operation. Thus, analysing the energy consumption pattern of a building could help in the design of potential energy savings and also in operation fault detection, while contributing to provide proper indoor environmental conditions to the building's occupants.This paper aims at presenting the main results of a study consisting in forecasting the hourly heating and cooling demand of an office building located in Lisbon, Portugal, using machine learning models and analysing the influence of exogenous variables on those predictions. In order to forecast the heating and cooling demand of the considered building, some traditional models, such as linear and polynomial regression, were considered, as well as artificial neural networks and support vector regression, oriented to machine learning. The input parameters considered in the development of those models were the hourly heating and cooling energy historical records, the occupancy, solar gains through glazing and the outside dry-bulb temperature.The models developed were validated using the mean absolute error (MAE) and the root mean squared error (RMSE), used to compare the values obtained from machine learning models with data obtained through a building energy simulation performed on an adequately calibrated model.The proposed exploratory analysis is integrated in a research project focused on applying machine learning methodologies to support energy forecasting in buildings. Hence, the research line proposed in this article corresponds to a preliminary project task associated with feature selection/extraction and evaluation of potential use of machine learning methods.
- Assessing the relevance of load profiling information in electrical load forecasting based on neural network modelsPublication . Sousa, J.C.; Neves, L.P.; H.M. JorgeThe article is focused on evaluating the relevance of load profiling information in electrical load forecasting, using neural networks as the forecasting methodology. Different models, with and without load profiling information, were tested and compared, and, the importance of the different inputs was investigated, using the concept of partial derivatives to understand the relevance of including this type of data in the input space. The paper presents a model for the day ahead load profile prediction for an area with many consumers. The results were analyzed with a simulated load diagram (to illustrate a distribution feeder) and also with a specific output of a 60/15 kV real distribution substation that feeds a small town. The adopted methodology was successfully implemented and resulted in reducing the mean absolute percentage error between 0.5% and 16%, depending on the nature of the concurrent methodology used and the forecasted day, with a major benefit regarding the treatment of special days (holidays). The results illustrate an interesting potential for the use of the load profiling information in forecasting.