2.1. Determine consumers' profiles

Determining dynamic profiles for consumers represents a challenge for energy suppliers due to the widespread implementation of smart metering (SM). Comparing with previous period before SM implementation, the consumers can play an active role, having the opportunity to control and schedule their consumption by programming some devices such as washing machine, electric heating, ventilation systems or car batteries. Another aspect that can be considered is to use micro-generation sources (photovoltaic panels installed on the roofs or buildings' facades or small wind turbines) to unload back into the grid the generated electricity according to the tariff systems. Based on activities that are carried out by consuming electricity, final consumers are categorized into household and non-household consumers. Final nonhousehold consumers are characterized by specific consumption, defined by profiles (consumption curves) defined in Romania by procedure [3] and are split into several categories such as industry, gas stations, civil works, hospitals, public utilities, hotels, retailers, etc. For household consumers in Romania, there is no official procedure or study used by energy suppliers, although SM is targeted for 80% implementation until 2020. Based on international studies such as [4] conducted in UK during 2010–2011 by the Department of Energy and Climate Change (DECC), the Department for the Environment Food and Rural Affairs (DEFRA) and the Energy Saving Trust (EST), demonstrated clearly the influence of time-of-use tariff (ToUT) on domestic demand response. The consumers shifted their evening peak consumption due to the various ToUT prices without significantly affecting their comfort and lifestyle. In Ref. [5], it is considered a multivariate statistical lifestyle analysis of household consumers in US. The study identified five factors reflecting social and behavioral profiles (patterns) determined by air conditioning, washing machines, climate conditions, PC and TV use. A detailed analyses for market segmentation is presented in Ref. [6] based on effect of lifestyles, socio-demographic factors, smart appliances, electricity and heat supply. Authors also identified the most influenced factors that determined the segmentation: socio-demographic factors (household size, net income and employment status), types of electric appliances and the use of new (smart) technologies. They also correlated the link between socio-demographic factors and the use of new technologies and smart appliances. The household profiles are determined in Ref. [7] by applying a time series auto-regression model—Periodic Autoregression with eXogenous variables (PARX) algorithm, taking into consideration temperature and occupants' daily habits. Thus, the consumption is correlated with life style influenced by temperature that leads to air conditioning and heating electricity consumption variations.

In 2012, European Commission adopted Energy Efficiency Directive that proposed measures to increase with 20% energy efficiency target by 2020 [2]. On November 30, 2016, the Commission updated the Directive, by targeting 30% energy efficiency for 2030. The proposed measures for energy are oriented toward increasing consumers' awareness regarding their consumption management through electronic bills and information and communications technology (ICT) solutions, encourage them to become prosumers by investing in their own generation sources such as photovoltaic (PV) panels, wind turbines and storage devices.

The main objective of the chapter is to present an implementation of artificial neural networks (ANNs) for the electricity consumption management based on smart metering (SM) data. This

• determining consumers' profiles and patterns with clustering and self-organizing maps

• forecasting aggregated electricity consumption for short-term period on a typical week

• forecasting energy generation for small wind turbines and photovoltaic panels installed at

• presenting the main components of an informatics prototype that allows the prosumers to configure and schedule their appliances in an interactive manner to optimize the electric-

The ANN performance will be compared with stochastic methods (classification, ARMA and

Regarding ICT solutions, the most important measures to reduce the energy poverty and to increase consumers' awareness toward energy efficiency concern both electricity suppliers and consumers. For electricity suppliers, market segmentation can be used to determine dynamic consumes' profiles to better understand consumption behavior and also to set up strategies and plans for different consumers groups. Another important measure for electricity suppliers consists in consumption (load) forecasting for short and medium term, used for planning the grid resources and wholesale electricity markets bids. For consumers, with the introduction of smart metering (SM) systems, their awareness increased and new methods must be taken into consideration such as consumption optimization of household appliances through userfriendly interfaces, micro-generation (through photovoltaic panels, small wind turbines and storage devices), mobile applications for real time billing with detailed information regarding

Determining dynamic profiles for consumers represents a challenge for energy suppliers due to the widespread implementation of smart metering (SM). Comparing with previous period

objective will be reached by following topics:

120 Advanced Applications for Artificial Neural Networks

day with autoregressive (AR) neural networks;

appliances' consumption or own generation sources.

2.1. Determine consumers' profiles

consumers side (prosumers) with feed-forward neural networks;

ARIMA models) and the best solution is adopted for ICT prototype.

2. Current problems in electricity consumption and generation

(SOM);

ity consumption.

In Ref. [8], we analyzed several methods for profile calculation including fuzzy C-means clustering, autoregression with exogenous variables and multi-linear regression. For National Grid UK, the load profiles are determined with multiple regression taking into consideration seven variables such as noon temperature, two variables regarding sunset moments compare to 6 o'clock in the afternoon and four variables related to the week days [9]. Thus eight profiles are obtained that can be used further for electricity consumption forecasts and simulation.

In Ref. [10], authors applied autoregression on hourly consumption data measured for 1000 household consumers in Canada (Ontario). The household consumption represents 30% of the total consumption and its contribution to the peak load is important due to the ventilation or AC devices. For autoregression, the variables also include hourly temperature data measured at local stations and occupation degree of each house.

In Ref. [11], clustering methods are used for determining load profiles for 300 electricity consumers from Malaysia. Authors proposed C-means fuzzy clustering algorithm by using average consumption values, thus obtaining four clusters, each of them being split into subclusters for a better and detailed typical consumption profiles.

In Section 3.1, we proposed a new method based on self-organizing maps (SOM) that allows us to determine six profiles clearly delimited for consumers having the following types of consumption: heating, cooling, ventilation, interior lighting, exterior lighting, water heating, usual devices (washing machine, refrigerator) and other smaller devices (TV, audio and computer). These profiles were compared with other profiles obtained by stochastic methods such as clustering and classification.
