Cluster Analysis and Model Comparison Using Smart Meter Data.

Load forecasting plays a crucial role in the world of smart grids. It governs many aspects of the smart grid and smart meter, such as demand response, asset management, investment, and future direction. This paper proposes time-series forecasting for short-term load prediction to unveil the load forecast benefits through different statistical and mathematical models, such as artificial neural networks, auto-regression, and ARIMA. It targets the problem of excessive computational load when dealing with time-series data. It also presents a business case that is used to analyze different clusters to find underlying factors of load consumption and predict the behavior of customers based on different parameters. On evaluating the accuracy of the prediction models, it is observed that ARIMA models with the (P, D, Q) values as (1, 1, 1) were most accurate compared to other values

A Single Scalable LSTM Model for Short-Term Forecasting of Massive Electricity Time Series

This article belongs to the Special Issue Forecasting in Electricity Markets with Big Data and Artificial Intelligence.Most electricity systems worldwide are deploying advanced metering infrastructures to collect relevant operational data. In particular, smart meters allow tracking electricity load consumption at a very disaggregated level and at high frequency rates. This data opens the possibility of developing new forecasting models with a potential positive impact on electricity systems. We present a general methodology that can process and forecast many smart-meter time series. Instead of using traditional and univariate approaches, we develop a single but complex recurrent neural-network model with long short-term memory that can capture individual consumption patterns and consumptions from different households. The resulting model can accurately predict future loads (short-term) of individual consumers, even if these were not included in the original training set. This entails a great potential for large-scale applications as once the single network is trained, accurate individual forecast for new consumers can be obtained at almost no computational cost. The proposed model is tested under a large set of numerical experiments by using a real-world dataset with thousands of disaggregated electricity consumption time series. Furthermore, we explore how geo-demographic segmentation of consumers may impact the forecasting accuracy of the model.The authors gratefully acknowledge the financial support from the Spanish government through projects MTM2017-88979-P and PID2019-108311GB-I00/AEI/10.13039/501100011033, and from Fundación Iberdrola through “Ayudas a la Investigación en Energía y Medio Ambiente 2018”

Fault detection in low voltage networks with smart meters and machine learning techniques

25th International Conference on Electricity Distribution (CIRED 2019), junio, Madrid (Spain)Smart grid data analytics and artificial intelligence techniques are playing an increasingly critical role, becoming the focal point to understanding low voltage real-time grid performance. This new point of view, (advanced analytics in combination with electrical knowledge expertise), makes flexibility and efficiency in electrical grid management approach real. HDCE (Hidrocantábrico Distribución Eléctrica) is the Electrical Distribution System Operator for EdP (Electricity of Portugal) around Spain who supplies energy to 650.000 customers. Starting from 2012, this company has nowadays replaced 99% of traditional meters by smart meters. Based on the analysis of smart metering voltage alarms, recorded from EdP LV distribution network, an automatic learning system has been implemented that groups and orders these alarms helping the grid distribution operator to drive the network technicians to the right and more urgent places where a grid failure is happening, starts to happen or will happen