Expect: EXplainable Prediction Model for Energy ConsumpTion

Mouakher, Amira; Inoubli, Wissem; Ounoughi, Chahinez; Kő, Andrea

doi:10.3390/math10020248

Cited by 10 publications

(6 citation statements)

References 24 publications

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“…However, it should be noted that univariate time series require an individual approach to data imputation problems as they do not contain additional attributes. We deal with this situation in the data analyzed by us, which does not contain additional information, such as, for example, in studies [17], where weather data was an additional attribute.…”

Section: Discussionmentioning

confidence: 99%

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Analysis of Business Customers’ Energy Consumption Data Registered by Trading Companies in Poland

Kowalska-Styczeń

Owczarek

Siwy³

et al. 2022

Energies

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In this article, we analyze the energy consumption data of business customers registered by trading companies in Poland. We focus on estimating missing data in hourly series, as forecasts of this frequency are needed to determine the volume of electricity orders on the power exchange or the contract market. Our goal is to identify an appropriate method of imputation missing data for this type of data. Trading companies expect a specific solution, so we use a procedure that allows to choose the imputation method, which will consequently improve the accuracy of forecasting energy consumption. Using this procedure, a statistical analysis of the occurrence of missing values is performed. Then, three techniques for generating missing data are selected (missing data are generated in randomly selected series without missing values). The selected imputation methods are tested and the best method is chosen based on MAE and MAPE errors.

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Section: Discussionmentioning

confidence: 99%

“…Time series analysis is widely used in many management systems, in the areas of: transport systems, including urban transport [1-4] environment [5][6][7][8], medical data [9][10][11][12][13][14][15] and energy [16][17][18][19][20][21]. In this work, we analyze data on electricity consumption.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Business Customers’ Energy Consumption Data Registered by Trading Companies in Poland

Kowalska-Styczeń

Owczarek

Siwy³

et al. 2022

Energies

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“…In contrast, Mouakher et al [89] delved into the realm of explainable deep learning models (XAI) to address the "black box" nature of traditional neural networks. Their approach aimed to provide more transparency and an understanding of how these models make decisions.…”

Section: Framework and Workflowsmentioning

confidence: 99%

Advanced Digital Tools for Data-Informed and Performance-Driven Design: A Review of Building Energy Consumption Forecasting Models Based on Machine Learning

Di Stefano,

Ruta,

Masera

2023

Applied Sciences

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Cities and buildings represent the core of human life, the nexus of economic activity, culture, and growth. Although cities cover less than 10% of the global land area, they are notorious for their substantial energy consumption and consequential carbon dioxide (CO2) emissions. These emissions significantly contribute to reducing the carbon budget available to mitigate the adverse impacts of climate change. In this context, the designers’ role is crucial to the technical and social response to climate change, and providing a new generation of tools and instruments is paramount to guide their decisions towards sustainable buildings and cities. In this regard, data-informed digital tools are a viable solution. These tools efficiently utilise available resources to estimate the energy consumption in buildings, thereby facilitating the formulation of effective urban policies and design optimisation. Furthermore, these data-driven digital tools enhance the application of algorithms across the building industry, empowering designers to make informed decisions, particularly in the early stages of design. This paper presents a comprehensive literature review on artificial intelligence-based tools that support performance-driven design. An exhaustive keyword-driven exploration across diverse bibliographic databases yielded a consolidated dataset used for automated analysis for discerning the prevalent themes, correlations, and structural nuances within the body of literature. The primary findings indicate an increasing emphasis on master plans and neighbourhood-scale simulations. However, it is observed that there is a lack of a streamlined framework integrating these data-driven tools into the design process.

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“…In the energy domain, Mouakher [23] introduced a framework seeking to provide explainable daily, weekly, and monthly predictions for the electricity consumption of building users of a LSTM-based neural network model, based on consumption data as well as external information, and, in particular, weather conditions and dwelling type. To this extent, in addition to their predictive model, the authors also developed an explainable layer that could explain to users the particular, every time, forecast of energy consumption.…”

Section: Approaches Used For Explainable Demand Forecastsmentioning

confidence: 99%

Explainable Approaches for Forecasting Building Electricity Consumption

Sakkas,

Yfanti,

Shah

et al. 2023

Energies

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Building electric energy is characterized by a significant increase in its uses (e.g., vehicle charging), a rapidly declining cost of all related data collection, and a proliferation of smart grid concepts, including diverse and flexible electricity pricing schemes. Not surprisingly, an increased number of approaches have been proposed for its modeling and forecasting. In this work, we place our emphasis on three forecasting-related issues. First, we look at the forecasting explainability, that is, the ability to understand and explain to the user what shapes the forecast. To this extent, we rely on concepts and approaches that are inherently explainable, such as the evolutionary approach of genetic programming (GP) and its associated symbolic expressions, as well as the so-called SHAP (SHapley Additive eXplanations) values, which is a well-established model agnostic approach for explainability, especially in terms of feature importance. Second, we investigate the impact of the training timeframe on the forecasting accuracy; this is driven by the realization that fast training would allow for faster deployment of forecasting in real-life solutions. And third, we explore the concept of counterfactual analysis on actionable features, that is, features that the user can really act upon and which therefore present an inherent advantage when it comes to decision support. We have found that SHAP values can provide important insights into the model explainability. In our analysis, GP models demonstrated superior performance compared to neural network-based models (with a 20–30% reduction in Root Mean Square Error (RMSE)) and time series models (with a 20–40% lower RMSE), but a rather questionable potential to produce crisp and insightful symbolic expressions, allowing a better insight into the model performance. We have also found and reported here on an important potential, especially for practical, decision support, of counterfactuals built on actionable features, and short training timeframes.

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Expect: EXplainable Prediction Model for Energy ConsumpTion

Cited by 10 publications

References 24 publications

Analysis of Business Customers’ Energy Consumption Data Registered by Trading Companies in Poland

Analysis of Business Customers’ Energy Consumption Data Registered by Trading Companies in Poland

Advanced Digital Tools for Data-Informed and Performance-Driven Design: A Review of Building Energy Consumption Forecasting Models Based on Machine Learning

Explainable Approaches for Forecasting Building Electricity Consumption

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