Kleyton da Costa

Analysis and forecasting of the industrial sector electricity consumption is important for energy planning and control, in addition to being essential for the developing of a country or region. In this context, electricity consumption projections are highly relevant for the decision-making of companies operating in energy systems with the aim of optimizing such operations. This paper proposes a methodology for forecasting electricity consumption by integrating a time series filtering method with any classical forecasting model. In particular, in order to evaluate the performance of the proposed methodology it was used the Seasonal and Trend decomposition using Loess (STL) decomposition method integrated with several classical statistical models (Holt-Winters, seasonal autoregressive integrated moving average, dynamic linear model, and TBATS) and the artificial neural networks approach (NNAR - neural network autoregression, MLP - multi-layer perceptron, and LSTM - long short-term memory). The methodology was applied to the electricity consumption of Brazil’s cement industry. Based on the MAPE and RMSE precision metrics, the proposed methodology obtained the best performance for the time series under analysis via introducing the filtering method. The LSTM model integrated with the STL decomposition presented the best results among the models considered.

The large-scale deployment of AI models introduces significant uncertainty regarding their systemic behaviour. While these models have the potential to boost productivity and advance fields such as health and climate science, they also pose risks, including the reinforcement of biases, the spread of misinformation, and support for autocratic regimes. This paper examines the interaction between social, environmental, and AI systems from the perspective of a complex system’s agent framework, using a resource-impact lens to explore these dynamics.

The rapid integration of artificial intelligence (AI) into various industries has introduced new challenges in governance and regulation, particularly regarding the understanding of complex AI systems. A critical demand from decision-makers is the ability to explain the results of machine learning models, which is essential for fostering trust and ensuring ethical AI practices. In this paper, we develop nine distinct model-agnostic metrics designed to quantify the extent to which model predictions can be explained. These metrics measure different aspects of model explainability, ranging from local importance, global importance, and surrogate predictions, allowing for a comprehensive evaluation of how models generate their outputs. Furthermore, by computing our metrics, we can rank models in terms of explainability criteria such as importance concentration and consistency, prediction fluctuation, and surrogate fidelity and stability, offering a valuable tool for selecting models based not only on accuracy but also on transparency. We demonstrate the practical utility of these metrics on classification and regression tasks, and integrate these metrics into an existing Python package for public use.

Deep learning models are being utilised increasingly within finance. Given the models are opaque in nature and are now being deployed for internal and consumer facing decisions, there are increasing concerns around the trustworthiness of their results. We test the stability of predictions and explanations of different deep learning models, which differ between each other only via subtle changes to model settings, with each model trained over the same data. Our results show that the models produce similar predictions but different explanations, even when the differences in model architecture are due to arbitrary factors like random seeds. We compare this behaviour with traditional, interpretable, ’glass-box models’, which show similar accuracies while maintaining stable explanations and predictions. Finally, we show a methodology based on network analysis to compare deep learning models. Our analysis has implications for the adoption and risk management of future deep learning models by regulated institutions.

Forecasting the industry’s electricity consumption is essential for energy planning in a given country or region. Thus, this study aims to apply time-series forecasting models (statistical approach and artificial neural network approach) to the industrial electricity consumption in the Brazilian system. For the statistical approach, the Holt–Winters, SARIMA, Dynamic Linear Model, and TBATS (Trigonometric Box–Cox transform, ARMA errors, Trend, and Seasonal components) models were considered. For the approach of artificial neural networks, the NNAR (neural network autoregression) and MLP (multilayer perceptron) models were considered. The results indicate that the MLP model was the one that obtained the best forecasting performance for the electricity consumption of the Brazilian industry under analysis.