Commercial buildings are a significant consumer of energy worldwide. Logistics facilities, and specifically warehouses, are a common building type which remain under-researched in the demand-side energy forecasting literature. Warehouses have an idiosyncratic profile when compared to other commercial and industrial buildings with a significant reliance on a small number of energy systems. As such, warehouse owners and operators are increasingly entering energy performance contracts with energy service companies (ESCOs) to minimise environmental impact, reduce costs, and improve competitiveness. ESCOs and warehouse owners and operators require accurate forecasts of their energy consumption so that precautionary and mitigation measures can be taken. This paper explores the performance of three machine learning models (Support Vector Regression (SVR), Random Forest, and Extreme Gradient Boosting (XGBoost)), three deep learning models (Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)), and a classical time series model, Autoregressive Integrated Moving Average (ARIMA) for predicting daily energy consumption. The dataset comprises 8040 records generated over an 11-month period from January to November 2020 from a non-refrigerated logistics facility located in Ireland. The grid search method was used to identify the best configurations for each model. The proposed XGBoost models outperformed other models for both very short-term load forecasting (VSTLF) and short-term load forecasting (STLF); the ARIMA model performed the worst.

To minimise environmental impact, to avoid regulatory penalties, and to improve competitiveness, energy-intensive manufacturing firms require accurate forecasts of their energy consumption so that precautionary and mitigation measures can be taken. Deep learning is widely touted as a superior analytical technique to traditional artificial neural networks, machine learning, and other classical time-series models due to its high dimensionality and problem-solving capabilities. Despite this, research on its application in demand-side energy forecasting is limited. We compare two benchmarks (Autoregressive Integrated Moving Average (ARIMA) and an existing manual technique used at the case site) against three deep-learning models (simple Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)) and two machine-learning models (Support Vector Regression (SVR) and Random Forest) for short-term load forecasting (STLF) using data from a Brazilian thermoplastic resin manufacturing plant. We use the grid search method to identify the best configurations for each model and then use Diebold–Mariano testing to confirm the results. The results suggests that the legacy approach used at the case site is the worst performing and that the GRU model outperformed all other models tested.

Commercial buildings are a significant consumer of energy worldwide. Logistics facilities, and specifically warehouses, are a common building type yet under-researched in the demand-side energy forecasting literature. Warehouses have an idiosyncratic profile when compared to other commercial and industrial buildings with a significant reliance on a small number of energy systems. As such, warehouse owners and operators are increasingly entering in to energy performance contracts with energy service companies (ESCOs) to minimise environmental impact, reduce costs, and improve competitiveness. ESCOs and warehouse owners and operators require accurate forecasts of their energy consumption so that precautionary and mitigation measures can be taken. This paper explores the performance of three machine learning models (Support Vector Regression (SVR), Random Forest, and Extreme Gradient Boosting (XGBoost)), three deep learning models (Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)), and a classical time series model, Autoregressive Integrated Moving Average (ARIMA) for predicting daily energy consumption. The dataset comprises 8,040 records generated over an 11-month period from January to November 2020 from a non-refrigerated logistics facility located in Ireland. The grid search method was used to identify the best configurations for each model. The proposed XGBoost models outperform other models for both very short load forecasting (VSTLF) and short term load forecasting (STLF); the ARIMA model performed the worst.

The development of electronic design evolves every day. The adequate selection of electronic components is at the heart of a project's success. Therefore, it is necessary to make a preliminary study of the characteristics of the components to be used in order to select the most appropriate ones for each application. For a smart plug project, an essential part is the current measurement system where the transducer represents a critical component. In this context, this paper makes a comparative study among three current transducer technologies: hall effect sensor, current transformer (CT) and shunt resistor. The applied current was measured by conducting a test using an existing sensor available on the market for each technology. In addition, a comparative analysis of the characteristics of the transducers was carried out and for some transducers both qualitative and quantitative analysis were undertaken. Finally, a multicriteria technique is used in the selection of transducers, taking into consideration the weight defined for each criteria in the project, using the mathematical process ROC (Rank Order Centroid). From the analysis, we can state that the hall effect sensor presents the best results when compared to the other transducers.

Título: Forecasting of Wind Power Generation Cristalândia wind farm using Tree-Based Machine Learning Approaches Autores: Camila Albuquerque Fitipaldi, Rodrigo de Paula Monteiro, Diego Pinheiro, Waldemar Ferreira, Liliane Sheyla da Silva Fonseca, Andrea Maria Nogueira Cavalcanti Ribeiro Resumo: Wind power has Read More ...

Overfitting jeopardizes deep learners dealing with noisy medical data. Despite its importance, our understanding of how neural networks generalize in medical imaging remains elusive. In this work, we propose to characterize the generalization profile of a model by evaluating its F1-score under data with varying noise levels. We use two architecturally distinct neural models, a Multilayer Perceptron (MLP) and a Convolutional Neural Network (CNN), to classify brain tumor in Magnetic Resonance Images (MRI) in a independent test data with different levels of salt-and-pepper noise. Our results reveal a clear distinction between the generalization profile of MLPs and CNNs. We demonstrate that shallow models can perform comparably to deep models when regularization is neglected. We also show that deep neural models benefit more from noise injection than shallow neural models, regardless of the differences between training and test data distribution. Our study sheds light on the nature of generalization in neural networks, laying the groundwork for further investigations of their underlying learning processes.