Forecasting Italian electricity market prices using a Neural Network and a Support Vector Regression

Davo, Federica; Vespucci, Maria Teresa; Gelmini, Alberto; Grisi, Paolo; Ronzio, Dario

doi:10.23919/aeit.2016.7892764

Cited by 7 publications

(4 citation statements)

References 19 publications

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“…The clustering algorithm minimizes the sum of squared distances between all points and the cluster center. The calculation and approach to determine the k function clustering have the following steps as indicated in [15].…”

Section: K-means Algorithmmentioning

confidence: 99%

Integrating Smart Meter Load Profiling for Enhanced Data Insights and Patient Care

Kelati,

Plosila,

Tenhunen

2024

A Comprehensive Overview of Telemedicine [Working Title]

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The utilization of smart meter data has opened new frontiers in the healthcare sector by enabling the detection of health status and daily life activities. A new methodology has been proposed to differentiate between normal and abnormal energy consumption, which can be leveraged to determine the health status of consumers by analyzing load profiling. By visualizing human activities based on electricity usage, there is a growing interest in using smart meter data for detecting health status and Activities of Daily Living (ADLs). The chapter discussed, this innovative approach utilizes the K-Means algorithm to classify the data and identify patterns in the energy usage of individuals. By analyzing this data, potential health issues can be indicated, thereby assisting the healthcare sector in addressing the needs of the elderly population more effectively. This technology has the potential to revolutionize healthcare by providing a non-invasive and cost-effective way to monitor the health of individuals and enable early intervention in case of any health issues.

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Section: K-means Algorithmmentioning

confidence: 99%

Integrating Smart Meter Load Profiling for Enhanced Data Insights and Patient Care

Kelati,

Plosila,

Tenhunen

2024

A Comprehensive Overview of Telemedicine [Working Title]

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“…Support vector learning is based on simple ideas which originated in statistical learning theory [41]. The simplicity comes from the fact that support vector machines (SVMs) apply a simple linear method to the data but in a high-dimensional feature space nonlinearly related to the input space.…”

Section: Support Vector Regressionmentioning

confidence: 99%

Simulation Study on Clustering Approaches for Short‐Term Electricity Forecasting

Gajowniczek

Ząbkowski

2018

Complexity

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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: (1) using data from 46 homes in Austin, Texas, the similarity measures from different time series are analyzed; (2) the optimal number of clusters for representing residential electricity use profiles is determined; and (3) 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.

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“…Support Vector learning is based on simple ideas which originated in statistical learning theory [31]. The simplicity comes from the fact that Support Vector Machines (SVMs) apply a simple linear method to the data but in a high-dimensional feature space non-linearly related to the input space.…”

Section: Support Vector Classificationmentioning

confidence: 99%

Revealing Household Characteristics from Electricity Meter Data with Grade Analysis and Machine Learning Algorithms

2018

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In this article, the Grade Correspondence Analysis (GCA) with posterior clustering and visualization is introduced and applied to extract important features to reveal households’ characteristics based on electricity usage data. The main goal of the analysis is to automatically extract, in a non-intrusive way, number of socio-economic household properties including family type, age of inhabitants, employment type, house type, and number of bedrooms. The knowledge of specific properties enables energy utilities to develop targeted energy conservation tariffs and to assure balanced operation management. In particular, classification of the households based on the electricity usage delivers value added information to allow accurate demand planning with the goal to enhance the overall efficiency of the network. The approach was evaluated by analyzing smart meter data collected from 4182 households in Ireland over a period of 1.5 years. The analysis outcome shows that revealing characteristics from smart meter data is feasible, and the proposed machine learning methods were yielding for an accuracy of approx. 90% and Area Under Receiver Operating Curve (AUC) of 0.82.

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Forecasting Italian electricity market prices using a Neural Network and a Support Vector Regression

Cited by 7 publications

References 19 publications

Integrating Smart Meter Load Profiling for Enhanced Data Insights and Patient Care

Integrating Smart Meter Load Profiling for Enhanced Data Insights and Patient Care

Simulation Study on Clustering Approaches for Short‐Term Electricity Forecasting

Revealing Household Characteristics from Electricity Meter Data with Grade Analysis and Machine Learning Algorithms

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