2.3. Consumption optimization

average consumption values, thus obtaining four clusters, each of them being split into sub-

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

Regression is seen as part of the first generation of consumption (load) forecasting methods. It is one of most widely used statistical methods due to its undoubtable advantages such as simplicity and transparency. For electricity load forecasting, regression methods are usually applied to effectively model the relationship of consumption level and other factors such as weather (i.e. temperature, humidity, etc.), day type (workdays and holidays) and consumers

Several methods based on regression have been used for short-term load forecasting with different levels of success such as ARMAX models [12], multiple regression [13, 14] and

In Ref. [18], authors describe several regression models for the next day peak forecasting. Their models incorporate deterministic influences such as weekend days, stochastic influences such as historical loads, and exogenous factors influences such as temperature. In papers [19–22],

According to [23, 24], ARIMA models have proven appropriate for forecasting electricity

In Section 3.2, we proposed a method based on autoregressive neural networks for short-term forecasting the electricity consumption aggregated at supplier's level for a typical day of the week. The forecasting method is applied on each profile previously determined by SOM. Also, we considered ARIMA method for load forecasting and at the end of Section 3.2, we compared

Regarding the micro-generation forecasting (small wind turbines and photovoltaic panels installed at consumers' side), the methods depend on the time interval. For example, stochastic methods (persistence and autoregressive patterns) are recommended in Ref. [25] for very short-term prediction (up to 4–6 hours). In addition, other authors [26] proposed Kalman integrated support vector machine (SVM) method to achieve a 10% accuracy improvement by comparing with artificial neural networks or autoregressive (AR) methods. Also a consistent approach is given by the use of ANNs for short-term generation forecast in case of wind turbines and photovoltaic (PV) panels. Various ANN-based algorithms are described in [27, 28], it is proposed Bayesian Regularization algorithms for forecasting. Also in [29, 30], authors proposed

authors described other applications of regression models to load forecasting.

back propagation neural networks based on the optimization of Swarm particles.

clusters for a better and detailed typical consumption profiles.

2.2. Consumption and micro-generation short-term forecasting

as clustering and classification.

122 Advanced Applications for Artificial Neural Networks

regression with neural networks [15–17].

profiles.

consumption.

the results for both methods.

Residential consumers usually have certain types of appliances: washing machine, dryer, dish machine, water heater, refrigerator, electric oven/grill, blender, iron machine, electric centralized heating system, coffee maker, vacuum cleaner, AC/ventilation systems, TV and other multimedia appliances. Out of these appliances only some of them can be automatically controlled and used at certain time intervals when electricity price is lower (e.g. washing machine, dish machine, electric oven, car batteries can be charged at night). Electricity suppliers may use several methods for consumption optimization using different optimization functions. In Ref. [32], it is described as stochastic optimization based on Monte Carlo simulation for minimization of estimated payment for entire day. Authors proposed a mixed integer linear programing (MILP) algorithm for optimization of the electricity residential consumption taking into account real time tariffs. In [33, 34], authors proposed genetic algorithms for consumption optimization. We proposed in Ref. [35] a method based on MILP with two optimization functions: cost-based function that minimizes the electricity costs depending on the time-of-use tariffs (ToUT) and a peak-shaving optimization function that minimizes the peak consumption. Both functions provide savings to electricity bills for consumers, but the second method also brings benefits for electricity suppliers and grid operators. In Section 5, we presented informatics solution that integrates the optimization methods presented in Ref. [35] and allows consumers to schedule their appliances in order to minimize the electricity costs.

## 2.4. Smart applications for real time billing

Until the expansion of smart meters, consumers were charged based on meters' reads made by an electricity supplier employee on different time intervals (usually 2–3 months) and most of the time the bills are based on estimations determined by the energy supplier on historical data regarding each household's consumption. Therefore, the electricity consumer was unable to customize and adjust his consumption based on ToUT or schedule his appliances to avoid peak consumption because he cannot benefit from real time information and his behavior is not reflected in the demand management system in real time. Since the widespread implementation of smart meters in most European countries, various informatics solutions were developed by software companies or by energy suppliers in order to provide consumers accurate electricity bills, near real time. A review of the top utility billing software products is available in Ref. [36]. These solutions are user-friendly, accessible online through mobile devices, intuitive and ease to use even for ICT novices.

Besides information regarding the total consumption, the billing systems have to provide consumptions data for different type of appliances measured by SM or by other smart measurement devices. Thus, consumers can analyze their consumption for heating, cooling, washing, lighting and other home appliances and they can schedule it based on ToUT. In Section 5, we proposed an informatics solution that provides friendly user interfaces and integrates methods for consumption optimization and micro-generation forecasting for electricity consumers.
