Krzysztof Gajowniczek

Electricity forecasting on the individual household level enhanced based on activity patterns

Leveraging smart metering solutions to support energy efficiency on the individual household level poses novel research challenges in monitoring usage and providing accurate load forecasting. Forecasting electricity usage is an especially important component that can provide intelligence to smart meters. In this paper, we propose an enhanced approach for load forecasting at the household level. The impacts of residents’ daily activities and appliance usages on the power consumption of the entire household are incorporated to improve the accuracy of the forecasting model. The contributions of this paper are threefold: (1) we addressed short-term electricity load forecasting for 24 hours ahead, not on the aggregate but on the individual household level, which fits into the Residential Power Load Forecasting (RPLF) methods; (2) for the forecasting, we utilized a household specific dataset of behaviors that influence power consumption, which was derived using segmentation and sequence mining algorithms; and (3) an extensive load forecasting study using different forecasting algorithms enhanced by the household activity patterns was undertaken.

Short term electricity forecasting based on user behavior from individual smart meter data

Smart metering is a quite new topic that has grown in importance all over the world and it appears to be a remedy for rising prices of electricity. Forecasting electricity usage is an important task to provide intelligence to the smart gird. Accurate forecasting will enable a utility provider to plan the resources and also to take control actions to balance the electricity supply and demand. The customers will benefit from metering solutions through greater understanding of their own energy consumption and future projections, allowing them to better manage costs of their usage. In this proof of concept paper, our contribution is twofold: (1) we deal with short term electricity load forecasting for 24 hours ahead, not on the aggregate but on the individual household level what fits into the stream of Residential Power Load Forecasting (RPLF) methods; (2) we utilized a set of household behavioral data which significantly improved the forecasts accuracy.

Comparison of decision trees with Rényi and Tsallis entropy applied for imbalanced churn dataset

Two algorithms for building classification trees, based on Tsallis and Rényi entropy, are proposed and applied to customer churn problem. The dataset for modeling represents highly unbalanced proportion of two classes, which is often found in real world applications, and may cause negative effects on classification performance of the algorithms. The quality measures for obtained trees are compared for different values of α parameter.

Simulation Study on the Application of the Generalized Entropy Concept in Artificial Neural Networks

Artificial neural networks are currently one of the most commonly used classifiers and over the recent years they have been successfully used in many practical applications, including banking and finance, health and medicine, engineering and manufacturing. A large number of error functions have been proposed in the literature to achieve a better predictive power. However, only a few works employ Tsallis statistics, although the method itself has been successfully applied in other machine learning techniques. This paper undertakes the effort to examine the q-generalized function based on Tsallis statistics as an alternative error measure in neural networks. In order to validate different performance aspects of the proposed function and to enable identification of its strengths and weaknesses the extensive simulation was prepared based on the artificial benchmarking dataset. The results indicate that Tsallis entropy error function can be successfully introduced in the neural networks yielding satisfactory results and handling with class imbalance, noise in data or use of non-informative predictors.

Simulation Study on Clustering Approaches for Short-Term Electricity Forecasting

Advanced metering infrastructures such as smart metering have begun to attract increasing attention; a considerable body of research is currently focusing on load profiling and forecasting at different scales on the grid. Electricity time series clustering is an effective tool for identifying useful information in various practical applications, including the forecasting of electricity usage, which is important for providing more data to smart meters. This paper presents a comprehensive study of clustering methods for residential electricity demand profiles and further applications focused on the creation of more accurate electricity forecasts for residential customers. The contributions of this paper are threefold: using data from 46 homes in Austin, Texas, the similarity measures from different time series are analyzed; the optimal number of clusters for representing residential electricity use profiles is determined; and an extensive load forecasting study using different segmentation-enhanced forecasting algorithms is undertaken. Finally, from the operator’s perspective, the implications of the results are discussed in terms of the use of clustering methods for grouping electrical load patterns.

Electricity peak demand classification with artificial neural networks

Demand peaks in electrical power system cause serious challenges for energy providers as these events are typically difficult to foresee and require the grid to support extraordinary consumption levels. Accurate peak forecasting enables utility providers to plan the resources and also to take control actions to balance electricity supply and demand. However, this is difficult in practice as it requires precision in prediction of peaks in advance. In this paper, our contribution is the proposal of data mining scheme to detect the peak load in the electricity system at country level. For this purpose we undertake the approach different from time series forecasting and represent it as pattern recognition problem. We utilize set of artificial neural networks to benefit from accurate detection of the peaks in the Polish power system. The key finding is that the algorithms can accurately detect 96.2% of the electricity peaks up to 24 hours ahead.

Grade analysis for energy usage patterns segmentation based on smart meter data

The Grade Correspondence Analysis (GCA) with posterior clustering and visualization is introduced and applied to smart meter data on individual household level. The main task of this analysis is to reveal the latent structure of electricity usage patterns and to propose a two dimensional segmentation taking into account the usage of selected home appliances and time of their usage. This provides the solutions applicable in smart metering systems that can support usage forecasting and contribute to higher energy awareness.

Generalized Entropy Cost Function in Neural Networks

Artificial neural networks are capable of constructing complex decision boundaries and over the recent years they have been widely used in many practical applications ranging from business to medical diagnosis and technical problems. A large number of error functions have been proposed in the literature to achieve a better predictive power. However, only a few works employ Tsallis statistics, which has successfully been applied in other fields. This paper undertakes the effort to examine the \( q \)-generalized function based on Tsallis statistics as an alternative error measure in neural networks. The results indicate that Tsallis entropy error function can be successfully applied in the neural networks yielding satisfactory results.

Blind Source Separation for Improved Load Forecasting on Individual Household Level

This paper presents the improved method for 24 h ahead load forecasting applied to individual household data from a smart metering system. In this approach we decompose a set of individual forecasts into basis latent components with destructive or constructive impact on the prediction. The main research problem in such model aggregation is the proper identification of destructive components that can be treated as some noise factors. To assess the randomness of signals and thus their similarity to the noise, we used a new variability measure that helps to compare decomposed signals with some typical noise models. The experiments performed on individual household electricity consumption data with blind separation algorithms contributed to forecasts improvements.

Extended AMUSE Algorithm and Novel Randomness Approach for BSS Model Aggregation with Methodology Remarks

In this paper we propose application of extended AMUSE blind signal separation method to improve a model prediction. In our approach we assume, that results generated by any regression model usually include both constructive and destructive components. In case of a few models, some of the components can be common to all of them. Our aim is to find the basis elements via AMUSE algorithm and distinguish the components with the constructive influence on the modelling quality from the destructive ones. We extend the standard AMUSE algorithm for cases with strong noises. The crucial question is to determine number of delays used in separation process and define criterion for destructive components identification. We propose novel method of randomness analysis to solve above problems. Due to complexity of the whole BSS aggregation method we include some methodological remarks as the framework for proposed approach.