**1. Introduction**

With the current increase in global warming, the focus of energy dependency has moved towards renewable energy sources (RESs), which seemingly have zero emission of greenhouse gases. As the percentage of carbon footprint rises with the use of traditional sources of energy, such as coal, the utilization of solar or hydro energy helps in reducing carbon footprints, providing a green energy alternative [1,2]. In order to ensure a pollution free ecosystem, we must move towards the utilization of RESs. Hence, a proper investment in RESs is essential [1–3].

RESs are integrated to the existing grid infrastructure to satisfy the energy demand of consumers, reducing the need for power from the main grid [1]. At the same time, storage devices are essential to optimize the use of renewable energy by storing energy when available and supplying it to consumers according to their requirement. Hence, for an optimal use of the energy from RESs, it is important to predict the energy demand of customers based on historical measurements. However, the evaluation of energy demand from the

**Citation:** Dash, S.K.; Roccotelli, M.; Khansama, R.R.; Fanti, M.P.; Mangini, A.M. Long Term Household Electricity Demand Forecasting Based on RNN-GBRT Model and a Novel Energy Theft Detection Method. *Appl. Sci.* **2021**, *11*, 8612. https://doi.org/ 10.3390/app11188612

Academic Editor: Luis Hernández-Callejo

Received: 30 June 2021 Accepted: 13 September 2021 Published: 16 September 2021

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**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

consumer load side cannot be efficiently performed through any of the present energy meter reading techniques in India [4,5]. Many researchers have focused on various power measuring devices [6–8]. These literature studies point out that the dynamic consumer electricity requirement has become a challenge for the power grid and sudden inflation in power demand and future energy requirement by the various categories of load cannot be efficiently predicted.

To overcome this limitation, many forecasting techniques to be applied by using smart metering and control systems are proposed [9,10]. In this context, efficient strategies to address the current challenges should aim at analyzing the consumer's energy requirements in the past, forecasting the demand and finally generating and producing the energy [9–11]. Demand forecasting using time series can be useful for informing the control unit regarding the energy over time to be produced in the future [12–14]. This can significantly help to cope with the problem of managing the generation from different sources, such as RESs, produce and distribute power more efficiently according to demand, and lessen the fee of depletion.

In addition, to improve energy distribution efficiency and reduce financial losses, power theft detection has become necessary to reduce energy loss at the consumer as well as generation point. Due to the intensity of economic and financial losses it provides, power theft is a punishable offence across the globe [14–16]. Indeed, power theft has enhanced the financial losses of both the supply units and the consumers in many countries. As an example, the Indian governmen<sup>t</sup> is losing its financial integrity and is continuously working on this issue to produce the required devices and systems for proper grid operation and distribution. Nevertheless, the Indian governmen<sup>t</sup> has, as of yet, failed to eliminate the power theft practice. Hence, to curb such illegal practices, a proper theft tracking scheme is necessary [17,18]. Although many electricity meter schemes are introduced by researchers in the literature, an efficient solution for detecting the illegal power theft is desired. The work [19] deals with the internal power larceny issues by constantly tracking the data consistency to detect any kind of abnormal and sudden change in the meter reading. The societal impact of the work lies in the fact that the utilities can distinguish the source of theft and the real consumers. Ultimately, the Indian governmen<sup>t</sup> can limit energy losses by curbing the illegal practices of power felonies, which would favor the financial and economic stability of the country [20,21].

In this context, the contribution of the paper is twofold. The first contribution is analyzing and forecasting the energy demand of the domestic household. The daily consumption of a household is predicted using and comparing various forecasting models and the energy demand for the next few days over a specific period of time is determined. The proposed RNN-GBRT hybrid model shows more accurate forecasting performance than other models. Thereby, creating awareness among the consumers and the utilities with regard to their energy usage is the major focus. The second contribution is towards identifying the power theft by constantly tracking the data consistency to detect any kind of abnormal and sudden change in the energy meter reading.

The rest of the paper is organized as follows. Section 2 presents the literature survey, wherein various papers are analyzed with respect to the proposed research work. Additionally, some of the limitations of the papers are highlighted. Section 3 presents the algorithms for demand forecasting and power theft detection, while their implementation is described in Section 4, which presents various simulation results and demonstrates the superiority of the proposed prediction model and the efficacy of the power theft strategy. Finally, Section 5 concludes the work and highlights the future scope.

## **2. Related Works**

The issues related to variation in load demand at the consumer end have raised serious concerns for the grid utility provider. Therefore, analyzing the future energy requirement of the domestic end-user is essential both for short term and long term load demand management. Focusing on the raised problems, researchers have proposed different load demand forecasting models to resolve the issues related to dynamic consumer load demand. In this section, we have analyzed related works and their approaches for the load demand forecasting.

In this context, to analyze the consumer behavior and electricity consumption over time, ref. [10] proposes an innovative strategy based on cluster analysis with K-means algorithm. The authors have identified the time variable separated groups of individual electricity consumption patterns, which will help in predicting the consumer electricity requirements. Considering the climate change effect and utilization of clean energy resources for power generation, the long term load demand prediction and demand side managemen<sup>t</sup> has been addressed in [11] for the Taiwan government. The photovoltaic energy generation prediction and its demand have been analyzed by the authors of [12] by the application of R programming to estimate its efficiency for a Photovoltaic (PV) based microgrid. For environmental and economic security, energy demand managemen<sup>t</sup> and forecast are essential to satisfy the future energy grid needs [13]. Therefore, future energy demand prediction by the utilization of conventional and advanced intelligent methods has been discussed in [13,14] based on time series models and soft computing-based prediction models. In addition, energy demand prediction has also been studied for short term period applications. In particular, short term load forecasting is presented in [15,16] by studying residential behavior learning, explaining the decay of radial basis function (RBF) neural networks (DRNNs), and demonstrating that deep-learning forecasting framework-based models have higher efficiency than the traditional forecasting strategies. The utilization of a fuzzy logic regression method for short term load forecasting is presented in [17], using previous three-year data with high accuracy. For the intraday electricity demand prediction in highly developed countries from Europe has been discussed in [22]. This paper has focused on the shot term a day ahead prediction based on principal component analysis of the daily demand profiles. The short term load demand prediction by the application of deep learning network-based CNN and multi-layer bi-directional LSTM prediction method for a household has been analyzed in [23]. This methodology has shown its effectiveness in achieving the lowest value of root mean square error and mean square error for an individual household. The utilization of the LSTM Network based on artificial neural networks to predict the long term electricity demand in Poland is presented in [24]. This method focused on cost managemen<sup>t</sup> and has been adopted for strategic plan development in electric power system of Poland. As the deep learning approach has become popular among researchers for developing energy demand forecasting models, in [25], deep learning approaches including deep neural networks and long short term memory have been considered. This work extensively verified the efficiency of deep neural network-based prediction methods under different consumer electricity patterns. Bioinspired algorithmic models have also been adopted to develop efficient energy forecasting models. In this context, [26,27] have investigated the utilization of autoregressive neural network based on genetic algorithm and particle swarm optimization for development of energy prediction models. The adopted hybrid method shows its effectiveness in energy prediction compared with other bio inspired algorithms. Moreover, the Autoregressive integrated moving average (ARIMA) model is a popular methodology for load demand evaluation and forecasting [28], and hybrid ARIMA strategies have been studied and implemented to achieve higher efficiency in [28–31]. In particular, for a Brazilian northeast company [28], the energy demand based on natural gas has been studied and predicted by applying an ARIMA-ANN based hybrid methodology. Furthermore, for the prediction of a single day ahead energy price, ARIMA and Holt-Winters forecasting models have been implemented in [29], achieving results that overcome those of the traditional models. Authors in [30] have proposed adaptive online ensemble learning with a recurrent neural network (RNN)-ARIMA-based hybrid model to reach the target of load demand prediction. Similarly, an ARIMA-GBRT (gradient boosting regression tree)-based hybrid model has also been developed and implemented in [31] to simulate and forecast the electrical energy consumption of residential buildings. The GBRT technique is used in combination with

ARIMA in the proposed model to improve the forecast performance with respect to existing prediction models. The comparison of forecasting models is performed based on standard performance indicators and demonstrates the superiority of the proposed ARIMA-GBRT model.

In addition, other contributions have proposed methods to identify the energy theft detection in power grids. In this context, the authors in [32,33] have studied the load profile of various customers to expose the degree of nontechnical losses. The study on load profile and load demand provided satisfactory data to analyze the possibilities of energy theft at the distribution point. In [33,34], a solution to the fraud activity by implementing the technique of decision tree is discussed. The authors of [35] used a time series neural network to identify the source of power tampering activity. Moreover, a machine learning algorithm allows the comparison of various local households to determine the number of honest customers and the rate of illegal consumers. In [36], the authors applied various classification models on regular energy expenditure data and encoded data. A comparison of accuracies of the adopted models is also presented. In addition, contributions [37–41] have studied how the power theft phenomenon affects the consumers as well as the power grid.

The analysis of the related literature reveals that there is the lack of a unique methodology to predict energy consumption, especially for detecting illegal use of energy, and it does not emerge a suitable and effective strategy to be applied in this context. Therefore, the authors of this paper are motivated to analyze the performance of various demand forecasting techniques and propose an efficient method for energy demand prediction. In addition, in order to protect the domestic household from power theft, a unique methodology is adopted and analyzed.

#### **3. Demand Forecasting Models and Power Theft Detection Strategy**

In this section, the proposed architecture for the energy demand forecasting and power theft detection is presented in Figure 1, highlighting the main components and functionalities. In particular, the conventional forecasting models for demand forecasting in domestic household are the focus of this section. Existing models, as well as the proposed hybrid RNN-GBRT forecasting strategy, are presented and analyzed. The considered dataset for models training and testing is taken from a household in Sceaux (Paris) and includes a total of 727 daily energy consumption values from 16 December 2008 to 20 December 2017. Moreover, a new strategy is presented to provide an effective solution to the power theft detection problem.

**Figure 1.** Architecture of the energy demand forecasting system.
