An Improved Load Feature Extraction Technique for Smart Homes Using Fuzzy-Based NILM

Overview of non-intrusive load monitoring and identification techniques

The use of electrical energy in remote areas must be carried out efficiently. This study proposed a load monitoring method in remote areas. This study developed a Non-Intrusive Load Monitoring (NILM) based on Data-Flow Programming (DFP) by applying a bagging decision tree algorithm to conduct load disaggregation. This study built the DFP and the Graphical User Interface (GUI) in LabVIEW connected to power sensor ADE9153A on Arduino UNO via serial communication. This experiment was conducted on the LabVIEW 2020 running on an Intel i5 2400-3.1 GHz CPU, 16 GB RAM, and 64-bit operating system computer. This study produced a good performance of NILM with 0.9617 of accuracy and 0.9728 of f1-score. The proposed method of the NILM process was suitable for electricity in remote areas because the DFP used in the algorithm is easy to understand, easy to operate, and inexpensive to build. Finally, the NILM technique can improve the efficiency of used electrical energy in remote areas. By applying NILM, the operator can determine the priority of which devices should be ON or OFF at a particular time as needed. In addition, the NILM can contribute to the balancing of small and weak microgrids in scenarios of high renewable energy penetration.

Energy management using non-intrusive load monitoring techniques – State-of-the-art and future research directions

Non-Intrusive Load Monitoring (NILM) techniques are today of great interest for several applications contexts. Indeed, the possibility of exploiting these techniques for having energy consumption data in a unique aggregated metric is very attractive not only for the cost-saving (due to the installation of a reduced number of meters) but also for catching important information coming from the field and addressed to implement predictive maintenance paradigms on the devices of interest. As matter of fact, the continued analysis (by means of the NILM techniques) of each load behavior could enable in detecting of dangerous situations or also identify incoming slow faults occurring on the loads themselves. Although the NILM techniques have been deeply analyzed in the scientific literature, some lacks are related to the analysis of the impact of the measurement uncertainty on their performance as well as to the capability of correctly identifying the loads operating against the number of loads present. In this framework, this paper proposes a preliminary sensitivity analysis aimed at verifying the impact of the meters measurement uncertainty and the number of loads involved on the performance of a well-known NILM technique, i.e. the Combinatorial Optimization (CO). To this aim, several measurement accuracies, power metrics, and the number of loads have been taken into account. The achieved results prove how the considered quantities of influence generally affect the performance of CO and can also provide useful information to the designers of the measurement system as an example in defining the best feature to be used, the maximum measurement uncertainty for a given number of loads, able to warrant the target accuracy required by the specific application.

Dimension reduction for NILM classification based on principle component analysis

Applications of NILM Techniques to Energy Management and Assisted Living

Enhancing NILM classification via robust principal component analysis dimension reduction

Non-Intrusive load monitoring (NILM) is an emerging technology for extracting potent information from a consumer's electric load profile. The NILM techniques are gaining popularity among researchers as they reduce the requirement for external hardware for load monitoring. These techniques require only the smart meter's data, thus reducing the need for other measuring and sensing devices. In this paper, the research status of non-intrusive load monitoring has been reviewed. The utilisation of NILM techniques for event detection, load monitoring and energy disaggregation has been discussed in this paper. Furthermore, the research gap and future aspects are discussed. This paper has a great utility factor for upcoming researchers interested in NILM techniques.

In order to enable demand response schemes for residential and industrial users, it is crucial to be able to predict and monitor each component of the total power consumption of a household or of an industrial site over time. We used the cross-validation method which is a model validation technique for assessing how the results of a statistical analysis will generalize to an independent data set. It is mainly used in settings where the goal is prediction, and one wants to estimate how accurately a predictive model will perform in practice. We exploit Non-Intrusive Load Monitoring (NILM) techniques in order to provide behavior patterns of the variables identified. This work presents a review Non-Intrusive Load Monitoring (NILM) techniques and describe the results of recognition patterns used for the identification of electrical devices. The proposed method has been validated on an experimental setting and using direct measurements of appliances consumption, proving that it allows achieving a high level of accuracy in load disaggregation.

Link for citation: Zakamaldin A.A., Perevoshchikov F.V., Shilin A.A. Efficient use of electric energy when operating a ball mill with a constant rotation speed. Bulletin of the Tomsk Polytechnic University. Geo Аssets Engineering, 2023, vol. 334, no. 9, рр. 115-127. In Rus. The relevance. Ball mills are widely used in industry for grinding bulk materials, as well as in the last stages of ore crushing, where the required optimal particle sizes are achieved for enrichment. Although ball mills have a relatively simple design and cover extensive research, the industry is still struggling with the very low energy efficiency of the grinding process. As the retrofitting heavy equipment is the costliest approach, process optimization through control system is the most suitable for energy consumption reduction. As a rule, the feed rate is the main control variable for a mill with a constant rotation speed. Due to insufficient information content, outdated methods of analysis and control of the internal dynamics of the mill, control systems keep the target value of feed rate at a deliberately low level in order not to overload the mill. This article provides an explanation of how, in such a case, an increase in mill feed rate could affect the energy efficiency of grinding. The main aim: study of energy efficient modes of operation of stations; verification of models and confirmation of experimental data: the relationship between door loading and power consumption indicators; construction of buildings for energy efficient control of ball towers. Objects: the process of grinding in a ball tower with a constant rotation speed. Methods: mathematical description of the behavior of the electromechanical part of a machine that consumes current and works on a load in the form of spherical furniture; simulation modeling of the operation of the door electric drive; statistical analysis of production indicators. Results. The paper demonstrate the tendency to increase electricity consumption with a decrease in feed rate and vice versa, respectively for ball mills with a synchronous drive of 2500 kW based on modeling and experimental data. Increase in feed rate in the range of 10–40 t/h, there is a drop in active power by 0,48–1,57 %. The values of the specific power consumption of ~5–8 kWh/t during the grinding of apatite ore were obtained. An increase in productivity by 5 t/h on average leads to a decrease in the specific power consumption by 0,12 kWh/t. It is necessary to increase productivity in the operation of ball mills only after integrating measuring instruments into the control system of the mill, which allow an accurate assessment of the state of loading of the mill drum.

The efficient use of electrical energy (an important component of sustainability) has become increasingly important for electrical consumers (industrial and non-industrial) as we face the challenges of climate change and the need to protect the environment. This theme is essential for guaranteeing a secure and sustainable future for both present and future generations. The power quality and the efficiency of electrical energy are connected to each other. Some power quality problems are caused by natural and unpredictable events, but many disturbances affecting power quality are caused by suppliers and consumers. One of the most important parameters in power engineering is the power factor, which indicates the degree of efficient use of electrical energy. Harmonics is the most important dynamic component of power quality, which affects the operation of electrical equipment and, at the same time, reduces the power factor. Harmonic sources in power systems are generally associated with nonlinear loads. To analyze the operating of passive filters (series L, shunt LC, T type LCL), two groups of experiments (relevant consumers were chosen for the industry as well as from the household sector) were carried out with single-phase nonlinear consumers: in the first group of experiments, a variable-frequency drive is used to supply a three-phase induction motor with variable load; in the second group of experiments, compact fluorescent lamps and LED lamps were used. Following the experiments, it was found that the difficulty of calibrating coils (to size a filter), especially the coils with a core, and the change in electrical properties over time for capacitors. For a certain type of consumer, the improvement of the current waveform depends on the type of filter used, the possibility of improving the power factor (to use electrical energy efficiently), and the role of the source impedance, which is particularly important to improve the efficiency of passive filters. Through the appropriate choice of the passive filter, a decrease in the deforming regime is obtained, with a slight decrease in the active power, and by increasing the power factor, a decrease in the losses of electrical energy from the electrical networks is obtained, with direct implications for the emission of greenhouse gases.

Renewable energy sources are slowly but surely supplementing conventional sources of energy. These energy sources create a large number of micro-grids which become a part of the main grid. Smart meters are essential components in smart micro-grids and smart grids. Together they form a complex system which has become today an attractive area of research world-wide in view of enormous benefits of reliability and safety of power supply economically. Several studies in smart meter applications have shown that in residential buildings, energy consumption can be reduced up to 15% with better management. For this, a non-intrusive appliance load monitoring (NIALM) technique has been advocated which enables appliance identification to help better energy management. The identification is done using load signatures with appropriate algorithm. Several features of load signatures ranging from voltage and current profiles (steady states and transients) to a plethora of features such as active power (P) and reactive power (Q), power factor (pf), harmonic components (h), magnitudes of h (mh), current waveform (CW), eigen values, switching transient waveform, instantaneous admittance waveform, etc have been used in NIALM. The algorithms used vary from a `single algorithm' to `multiple algorithms', the latter used in `Committee Decision Mechanism' (CDM) in which majority of `votes' (i.e., agreement), `cast' by each of the algorithms, decides the final result of appliances identified. This scenario presents a serious difficulty with regard to choice of features and algorithms, besides the possibility of increasing the hardware and software costs of smart meters. The motivation for the study in this paper is provided by this scenario. The basic objective of this paper is to investigate the possibility of including only a limited number of features as load signature of appliances, and the use of only a single algorithm to effectively implement NIALM without any ambiguity. Results of our study so far indicate that generally only four features of P, Q, h and mh are sufficient for detection of appliances in operation; however, in some cases (~>10%), the additional feature, CW, may be needed for final identification.. The experiments conducted so far yielded unambiguous identification of appliances and the results are encouraging enough for possible use in practice for residential loads only.

Non-intrusive load monitoring (NILM) is to estimate individual appliance’s power consumption from aggregated smart meter data, which is useful for optimized energy management and provisioning of customized services. While deep learning (DL) has achieved state-of-the-art NILM performance, it is still constrained by the dependency on large amounts of data and intensive computations on training. In this paper, we propose a pre-training approach to address the generalization of DL models for NILM. We develop a meta-learning based approach and an ensemble learning based approach, which pre-train a base model and then fine-tune it with few-short learning when applied to an unknown dataset. The models are validated with two real-world datasets and shown to achieve a superior transferability performance compared with traditional DL and transfer learning methods.

This paper presents a complete real-time implementation of a Non-Intrusive Appliance Load Monitoring (NIALM) system that, is robust under residential voltage level fluctuations. Existing NIALM techniques rely on multiple measurements taken at high sampling rates, but, only have been proven in simulated environments without even considering the effect of residential voltage level fluctuations — which is a severe problem in power systems of most developing countries like Sri Lanka. In contrast, through the NIALM method proposed in this paper, accurate load monitoring results were obtained in realtime using only smart meter measurements taken at a low sampling rate from a real appliance setup under residential voltage level fluctuations. In the proposed NIALM method, initially in the learning phase, a properly constructed MATLABTM Graphical User Interface (GUI) was used to acquire signals of each appliance active power consumption and voltage levels. Then, obtained active power measurements were separated into subspace components (SCs) via the Karhunen Loeve' Expansion (KLE) while also taking the voltage variations into account. Using those SCs, a unique information rich appliance level signature database was constructed and it was then used to obtain the signatures for all possible device combinations. Next, a separate GUI was designed to identify the turned ON appliance combination in the current time window using the pre-constructed signature databases, after reading the total residential active power consumption and the supply voltage. To validate the proposed real-time NIALM implementation, data from a laboratory arrangement consisting of ten household appliances was used. From the results, it was found that the proposed method is capable of accurately identifying the turned on appliances even under severe residential supply voltage level fluctuations.

The rapid development of technology in the electrical energy sector within the last 20 years has led to growing electric power needs through the increased number of electrical appliances and automation of tasks. In parallel the global climate protection goals, energy conservation and efficient energy management arise interest for reduction of the overall energy consumption. These requirements have led to the recent adoption of smart-meters and smart-grids, as well as to the rise of Load Monitoring (LM) using energy disaggregation, also referred to as Non-Intrusive Load Monitoring (NILM), which enables appliance-specific energy monitoring by only observing the aggregated energy consumption of a household. The real-time information on appliance level can be used to get deeper insights in the origin of energy consumption and to make optimization, strategic load scheduling and demand management feasible. The three main contributions are as follows: First, a generalized up-to-date review of NILM approaches including a high-level taxonomy of NILM methodologies is provided. Second, previously published results are grouped based on the experimental setup which allows direct comparison. Third, the article is accompanied by a software implementation of the described NILM approaches.