Short- and Very Short-Term Firm-Level Load Forecasting for Warehouses: A Comparison of Machine Learning and Deep Learning Models

The transition to Electric Vehicles (EV) in place of traditional internal combustion engines is increasing societal demand for electricity. The ability to integrate the additional demand from EV charging into forecasting electricity demand is critical for maintaining the reliability of electricity generation and distribution. Load forecasting studies typically exclude households with home EV charging, focusing on offices, schools, and public charging stations. Moreover, they provide point forecasts which do not offer information about prediction uncertainty. Consequently, this paper proposes the Long Short-Term Memory Bayesian Neural Networks (LSTM-BNNs) for household load forecasting in presence of EV charging. The approach takes advantage of the LSTM model to capture the time dependencies and uses the dropout layer with Bayesian inference to generate prediction intervals. Results show that the proposed LSTM-BNNs achieve accuracy similar to point forecasts with the advantage of prediction intervals. Moreover, the impact of lockdowns related to the COVID-19 pandemic on the load forecasting model is examined, and the analysis shows that there is no major change in the model performance as, for the considered households, the randomness of the EV charging outweighs the change due to pandemic.

Section: Electricity Load Forecastingmentioning

confidence: 99%

“…The reviewed studies [6,8,12,[17][18][19][20][21][22][23] on generic load forecasting represent the state-ofthe-art in energy forecasting but their behavior in presence of EV charging has not been examined. Nevertheless, they represent a great foundation for forecasting EV charging load.…”

Section: Electricity Load Forecastingmentioning

confidence: 99%

Interval Load Forecasting for Individual Households in the Presence of Electric Vehicle Charging

Skala,

Elgalhud,

Grolinger

et al. 2023

“…These models process an input sequence at a time and keep hidden units in their state vector, which comprise knowledge about the history of all of the former components of the series. Nevertheless, RNNs have a gradient vanishing problem and hence cannot maintain long-range dependences [43].…”

Section: Rnnmentioning

confidence: 99%

An Ensemble Deep-Learning-Based Model for Hour-Ahead Load Forecasting with a Feature Selection Approach: A Comparative Study with State-of-the-Art Methods

Yaprakdal¹

2022

The realization of load forecasting studies within the scope of forecasting periods varies depending on the application areas and estimation purposes. It is mainly carried out at three intervals: short-term, medium-term, and long-term. Short-term load forecasting (STLF) incorporates hour-ahead load forecasting, which is critical for dynamic data-driven smart power system applications. Nevertheless, based on our knowledge, there are not enough academic studies prepared with particular emphasis on this sub-topic, and none of the related studies evaluate STLF forecasting methods in this regard. As such, machine learning (ML) and deep learning (DL) architectures and forecasters have recently been successfully applied to STLF, and are state-of-the-art techniques in the energy forecasting area. Here, hour-ahead load forecasting methods, the majority of which are frequently preferred high-performing up-to-date methods in the literature, were first examined based on different forecasting techniques using two different aggregated-level datasets and observing the effects of these methods on both. Case and comparison studies have been conducted on these high-performing methods before, but there are not many examples studied using data from two different structures. Although the data used in this study were different from each other in terms of the time step, they also had very different and varied features. In addition, feature selection was studied on both datasets and a backward-eliminated exhaustive approach based on the performance of the artificial neural network (ANN) on the validation set was proposed for the development study of the forecasting models. A new DL-based ensemble approach was proposed after examining the results obtained on two separate datasets by applying the feature selection approach to the working forecasting methods, and the numerical results illustrate that it can significantly improve the forecasting performance compared with these up-to-date methods.

“…ML is the science of getting computers to act without being explicitly programmed. In recent decades, ML has proven to be a powerful tool for deriving insights from data [33,34]. It has been applied successfully in self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome thanks to many practical algorithms developed.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

Recognition of Geothermal Surface Manifestations: A Comparison of Machine Learning and Deep Learning

Xiong

Zhu

et al. 2022

Geothermal surface manifestations (GSMs) are direct clues towards hydrothermal activities of a geothermal system in the subsurface and significant indications for geothermal resource exploration. It is essential to recognize various GSMs for potential geothermal energy exploration. However, there is a lack of work to fulfill this task using deep learning (DL), which has achieved unprecedented successes in computer vision and image interpretation. This study aims to explore the feasibility of using a DL model to fulfill the recognition of GSMs with photographs. A new image dataset was created for the GSM recognition by preprocessing and visual interpretation with expert knowledge and a high-quality check after downloading images from the Internet. The dataset consists of seven GSM types, i.e., warm spring, hot spring, geyser, fumarole, mud pot, hydrothermal alteration, crater lake, and one type of none GSM, including 500 images of different photographs for each type. The recognition results of the GoogLeNet model were compared with those of three machine learning (ML) algorithms, i.e., Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbor (KNN), by using the assessment metrics of overall accuracy (OA), overall F1 score (OF), and computational time (CT) for training and testing the models via cross-validation. The results show that the retrained GoogLeNet model using transfer learning has significant advantages of accuracies and performances over the three ML classifiers, with the highest OA, the biggest OF, and the fastest CT for both the validation and test. On the contrary, the three selected ML classifiers perform poorly for this task due to their low OA, small OF, and long CT. This suggests that transfer learning with a pretrained network be a feasible method to fulfill the recognition of the GSMs. Hopefully, this study provides a reference paradigm to help promote further research on the application of state-of-the-art DL in the geothermics domain.