Typical Load Patterns Research Articles

Load Pattern Analysis and Consumer Response Potential Evaluation for Demand‐Side Power Regulation

Flexible demand‐side load resources provide a novel approach for promoting power supply‐demand equilibrium and the consumption of renewable energy. However, the assessment of consumer response potential is encountering significant challenges due to uncertain surrounding users’ power consumption behavior and their individual preferences. In this paper, we propose a quantitative method for evaluating the load response potential of consumers based on nonnegative matrix factorization and prospect theory. Firstly, nonnegative matrix factorization is employed to decompose and cluster historical load data, enabling the extraction of typical load patterns for different consumer groups. In addition, three indicators are proposed to assess consumer response potential from dispatchable power capacity, available response time, and response reliability. Considering the bounded rationality and subjective preferences of consumers, this study employs gray correlation analysis and prospect theory to explore how consumers perceive risks and benefits across different indicators. Furthermore, subjective weights are incorporated to reflect the requirements of the power system side. Simulation results demonstrate the advantages of the proposed approach in the comprehensive evaluation of consumer response potential.

Reconstructing hourly residential electrical load profiles for Renewable Energy Communities using non-intrusive machine learning techniques

The successful implementation of Renewable Energy Communities (RECs) involves maximizing the self-consumption within a community, particularly in regulatory contexts in which shared energy is incentivized. In many countries, the absence of a metering infrastructure that provides data at an hourly or sub-hourly resolution level for low-voltage users (e.g., residential and commercial users) makes the design of a new energy community a challenging task. This study proposes a non-intrusive machine learning methodology that can be used to generate residential electrical consumption profiles at an hourly resolution level using only monthly consumption data (i.e., billed energy), with the aim of estimating the energy shared by RECs. The proposed methodology involves three phases: first, identifying the typical load patterns of residential users through k-Means clustering, then implementing a Random Forest algorithm, based on monthly energy bills, to identify typical load patterns and, finally, reconstructing the hourly electrical load profile through a data-driven rescaling procedure. The effectiveness of the proposed methodology has been evaluated through an REC case study composed by 37 residential users powered by a 70 kWp photovoltaic plant. The Normalized Mean Absolute Error (NMAE) and the Normalized Root Mean Squared Error (NRMSE) were evaluated over an entire year and whenever the energy was shared within the REC. The Relative Absolute Error was also measured when estimating the shared energy at both a monthly (MRAE) and at an annual basis. (RAE). A comparison between the REC load profile reconstructed using the proposed methodology and the real load profile yielded an overall NMAE of 20.04 %, an NRMSE of 26.17 %, and errors of 18.34 % and 23.87 % during shared energy timeframes, respectively. Furthermore, our model delivered relative absolute errors for the estimation of the shared energy at a monthly and annual scale of 8.31 % and 0.12 %, respectively.

An energy demand-side management and net metering decision framework

Demand side management (DSM) and net metering (NM) are two important strategies that can be used in smart energy management systems to help utilities reduce peak loads and increase the penetration of renewable energy, while also enabling customers to save energy and reduce energy bills. In order to effectively implement DSM and NM programs, it is important to identify the customers who are most likely to benefit from these programs and to quantify the economic impact and benefit for both utilities and end-users. This paper proposes a decision framework that utilizes smart meter data and clustering analysis to identify representative load patterns, and a five-stage method for segmenting load patterns to create a consumption intensity matrix. A fuzzy ensemble based multi-criteria decision making framework is then applied to identify the most potential customer groups and quantify the economic benefit and impact. This paper presents a novel approach for identifying potential customers for DSM and NM programs and developing personalized services for energy demand management. The proposed framework is evaluated through a case study, which demonstrates its robustness in decision making and identifies 13 typical load patterns, seven for weekdays and six for weekends, representing diverse consumption behaviors. The weekday load patterns perform better than the weekend load patterns in terms of the five-stage method.

An adaptive forecasting method for the aggregated load with pattern matching

Electrical load forecasting plays a vital role in the operation of power system. In this paper, a novel adaptive short-term load forecasting method for the aggregated load is built. The proposed method consists of two stages: load forecast model preparation stage and adaptive load forecast model selection stage. In the first stage, based on historical load data of all consumers, the typical monthly load patterns are firstly identified in an optimal fashion with the aid of the cosine similarity. Then, for each identified monthly load pattern, a stacking ensemble learning method is proposed to train the load forecasting model. In the second stage, according to the similarity between individual load data of the latest month and the identified monthly load pattern, all the consumers are firstly classified into different groups where each group corresponds to a particular load pattern. Then, for each group, the corresponding trained load forecasting model is employed for short-term load forecast and the final forecast of the aggregated load is calculated as a simple aggregation of the produced load forecast for each group of consumers. Case studies conducted on open dataset show that, compared with the single forecasting model, the proposed adaptive load forecasting method can effectively improve the load forecasting accuracy.

Defect Data Mining of Power Consumption Law Based on Improved K-Means Algorithm Clustering

With the further construction and development of the smart grid, in the process of power development, production, and use, as well as the process of power distribution and use, each link will produce some high-dimensional data on the power grid with huge volume, complex structure, and complex correlation among them. The distribution of high-dimensional data in space is different from that in low-dimensional space, and the computational cost increases dramatically, which increases the complexity of visualization of high-dimensional power consumption data. Clustering analysis is a way to cluster a large number of users and summarize the typical load characteristics of different types of users. How to determine the prior information conditions of data and how to select the clustering criteria become the key to clustering. Aiming at the problems of traditional clustering algorithms in the current feature clustering analysis, this paper first deals with the load through t-SNE dimensional reduction technology, then combines the GSA elbow criterion and dichotomous K-means algorithm to cluster the load, and finally summarizes three typical load features according to the clustering results. Effective data mining technology is used to cluster and divide the massive load characteristics efficiently, which will dynamically respond to and manage the demand side. The error of classification results is less than 4.28% through the example of load characteristics. The classification accuracy of the test is 12.2% higher than that of the traditional method. According to the experimental results, the characteristics of typical load patterns and the corresponding load curve characteristics are analyzed. It overcomes the dependence of the traditional K-means algorithm on the initial centroid, avoids the algorithm falling into local optimum, and plays an important role in the defect data mining of power consumption law in power enterprises.

Data-Driven Load Pattern Identification Based on R-Vine Copula and Random Forest Method

Massive residential power consumption information provides data support for the mining and analysis of load patterns. This article proposes a complete framework for load pattern identification, which mainly includes the clustering module and the classification module. Considering that the high-dimensional load profiling dataset will bring a heavy computational burden, multiple dimensional scaling is introduced in the process of data preprocessing. Then, an innovative mixture model based on regular vine copula mixture model (RVMM) is adopted for clustering typical load patterns. Finally, a random forest (RF) classifier constructed with certain load characteristic indexes and RVMM clustering results is employed as a supervised classification model to predict the categories of subsequent new customers, and the accuracy is calculated by the 10-fold cross-validation. It is demonstrated in the case study that the proposed RVMM algorithm exhibits better performance in the clustering validity evaluation. Besides, higher accuracy is achieved by the RF classifier.

Unsupervised classification model of residential power users based on typical load patterns

It is critical for the smart grid to mining the power users’ consumption behaviors. The accumulation of power data provides the possibility of conducting experiments for research focus on behavior analysis. This paper proposes a residential power users’ classification method based on typical load patterns called UCM-LP, to classify users’ power consumption behavior. The method firstly performs fuzzy grouping on the typical power consumption characteristics of multiple users through two-stage clustering, then divides cluster label to obtain the final power consumption categories of the user. Finally, this paper verified the algorithm using the real household power consumption data, obtaining the specific power consumption category patterns and discussed the differences among power consumption categories.

Characterizing residential load patterns on multi-time scales utilizing LSTM autoencoder and electricity consumption data

• We propose a novel framework to characterize residential load patterns utilizing LSTM-AE and electricity consumption data. • LSTM-AE model is designed for dimensionality reduction and feature extraction. • Two-level clustering method is proposed to discover TLPs and MLPs on multi-time scales. • TLPs profile resident's load patterns from a global view, MLPs characterize load patterns of individual and groups. • Consumer groups and residential energy lifestyles are revealed to customize personal demand response strategies. Load patterns represent a clear picture of electricity usage, reflecting the consumer's habits. Previous works mainly focused on load patterns discovery on a fixed scale, but limited to characterize load patterns on multi-time scales utilizing electricity consumption data (ECD). Therefore, we propose a novel framework to characterize residential load patterns on multi-time scales. The long-short-term memory autoencoder (LSTM-AE) model is designed for dimensionality reduction and feature extraction. Furthermore, a two-level clustering method is proposed to discover and characterize typical load patterns (TLPs) and multifaceted load patterns (MLPs) on multi-time scales. The proposed framework is comprehensively evaluated via extensive experiments on three real ECD. Results show that: (1) Reconstruction errors of LSTM-AE are lower than 6 benchmark models across different time scales, which validates the superiority of LSTM-AE. (2) TLPs and MLPs on daily, weekly, monthly and yearly scale are discovered by the two-level clustering method. TLPs profile the resident's electricity usages from a global view. (3) MLPs present the consumer segmentation and characterize residential load patterns of individual and groups. Especially, customer groups and electricity usage habits or lifestyles are revealed thoroughly to customize personal demand response strategies. This study can provide new valuable insights for smart grid applications.

Load pattern recognition based optimization method for energy flexibility in office buildings

Air conditioning systems are generally considered to have the greatest flexibility potential in buildings that can be flexibly regulated with thermal storage to reduce the interaction with the power grid and increase demand response benefits. In previous studies, the flexibility of air-conditioning systems was reflected through time-of-use tariffs. However, a strategy that only factors the tariffs incurs a greater operational energy consumption. In this study, a flexibility factor was established and incorporated into the multi-objective optimization process, together with the operational energy consumption, as two optimization objectives. After obtaining typical load patterns using a two-step clustering method, for multi-objective decision-making in the day-ahead operation, the entropy-grey technique for order preference by similarity to an ideal solution method is used. Considering an office building as a case study, we found that the optimized flexibility factor can reach 0.31 and 0.99 during a week of operation in winter and summer, on average, respectively, and achieved a cumulative energy-saving effect of 17.98% and 35.49%. In addition, the two-step clustering method can better demonstrate the flexibility factor than the single-step clustering.

Spatio-Temporal Two-Dimensions Data Based Customer Baseline Load Estimation Approach Using LASSO Regression

Customer baseline load (CBL) estimation plays a crucial role in financial settlement for incentive-based demand response (DR). Most current CBL estimation methods utilize temporal auto-correlation or spatial cross-correlation to estimate the CBL, which would produce large errors when the load pattern suddenly changes on the DR event day or the number of CONTROL customers is insufficient. To this end, a spatio-temporal CBL estimation approach is proposed to improve the estimation accuracy in this paper. First, all CONTROL customers are grouped into several non-overlapping clusters by the K-means algorithm. Each DR customer is then matched to the most similar cluster according to the similarity between the typical load pattern and the cluster center. Second, spatio-temporal features are extracted from the historical load data of the DR customer itself and the load data of the matched CONTROL customers on the DR event day. Third, the least absolute shrinkage and selection operator (LASSO) regression model is constructed to estimate the CBL of each DR customer. The effectiveness and superiority of the proposed approach have been verified on a real-world load dataset.

Graph convolutional network-based aggregated demand response baseline load estimation

Accurate aggregated baseline load (ABL) estimation is very important for the demand response (DR) compensation settlement between system operators and DR aggregators. Current ABL estimation methods totally ignore the spatial correlation between load patterns of different customers, which will lead to large errors when the load pattern on the DR event day fluctuates largely compared with that in historical days. To this end, this paper proposes a Graph Convolutional Network (GCN)-based ABL estimation method to improve the estimation accuracy. The basic idea is to enhance the estimator's ability to capture load uncertainty by sharing load fluctuation information between different customers. The proposed method contains three main steps: First, all customers are grouped into different clusters by the K-means algorithm according to their historical typical load patterns (TLPs). Second, these clusters are transformed into an undirected graph based on an adjacency matrix reflecting the spatial correlations, which are constructed according to the difference between the TLPs in different clusters. Third, the ABL estimation is transformed into a node regression problem of the graph. Case studies on a real load dataset verify the effectiveness and superiority of the proposed method.

A method of mining electricity consumption behaviour based on CC-DWT

In this paper, a fusion clustering algorithm based on discrete wavelet transform called CC-DDWT is proposed. In this method, the preliminary feature construction of load curve is realized by multi-layer discrete wavelet transform, and the approximate component and detail component are formed, and then the clustering fusion is carried out. The experimental results show that the typical load patterns of a single user extracted by this method are more reliable than the traditional methods.

An unsupervised data mining strategy for performance evaluation of ground source heat pump systems

This paper presents an efficient data mining strategy to reveal the operational problems of ground source heat pump (GSHP) systems. Symbolic Aggregate approXimation was employed to identify typical daily load patterns and provide a reference for data partition. Kernel Density Estimation was used to evaluate overall system performance in different operation patterns identified. To find the root causes of inefficient operations, a customized association rule mining model was developed to discover the associations among different attributes. Lastly, the inference tree technique was applied to demonstrate the discovered associations for better comparative analysis. This strategy was evaluated using one-year operational data of a GSHP system. It was found that only 25% of the operations of this GSHP system in cooling seasons during 00:00–06:00 were considered as energy efficient, while above 79% of the operations in heating seasons were considered as energy efficient. Excessive power consumption of the water pumps due to improper control resulted in poor system COP (i.e. less than 2.0 or even 1.4 in cooling seasons and less than 2.1 in heating seasons). The results showed that this strategy can efficiently discover useful information from the operational data and identify energy conservation opportunities of GSHP systems.

Optimum Values of Tank Volume to Collector Area Ratios of Thermosyphon Solar Water Heaters for Libyan Families

Thermosyphon solar water heaters are the best choice to be utilized in residential sector to provide the required hot water in Libya. These systems are autonomy in operation and as a result require less maintenance and hence low operation and initial costs than active system. ln this paper, GenOpt optimization technique provided in TRNSYS simulation program is used for sizing Thermosyphon systems to obtain the optimum size (namely V/A ratio) of Thermosyphon system that suits Libyan families according to the weather and operating conditions of Tripoli. The typical hot water load pattern and quantity of the Libyan families are taken from a field study conducted on a number of solar system for a whole year. Whereas, the typical weather data are taken from five year measurements recorded at CSERS weather station. The results showed that the optimum storage tank volume to collector area ratio of Thermosyphon systems is between 49-60 Lit/m2 for the most common collector characteristics ratio (equation!!) and the auxiliary heater set point temperature ranges from (45-60C).

A data mining-based framework for the identification of daily electricity usage patterns and anomaly detection in building electricity consumption data

With the development of advanced information techniques, smart energy meters have made a considerable amount of real-time electricity consumption data available. These data provide a promising way to understand energy usage patterns and improve building energy management. However, previous studies have paid more attention to methodologies for the identification of energy usage patterns and are limited in the interpretability and applications of the patterns. In this context, this paper proposes a general data mining-based framework that can extract typical electricity load patterns (TELPs) and discover insightful information hidden in the patterns. The framework integrates multiple data mining techniques and mainly consists of three phases: data preparation, identification of TELPs and knowledge discovery in the patterns. A new clustering method with a two-step clustering analysis is proposed to identify the TELPs at the individual building level. Before clustering, five statistical features that represent the shapes of electricity load profiles are first defined to reduce the dimensions of daily electricity load profiles. The first clustering step aims at detecting outliers of daily electricity load profiles (DELPs) by using the density-based spatial clustering application with noise (DBSCAN) algorithm clustering technique, which addresses the data quality issues for electricity consumption data derived from energy consumption monitoring platforms (ECMPs). The second clustering step aims at grouping similar DELPs by means of the k-means algorithm to extract TELPs. The effectiveness of the proposed clustering method is demonstrated by a comparison with two single-step clustering techniques. Furthermore, a classification and regression tree (CART) algorithm is employed to discover insightful knowledge on TELPs and improve the interpretability of clustering results, namely, to explain the relations between dynamic influencing factors related to electricity consumption and TELPs. The proposed framework is applied to analyze the time-series electricity consumption data of three practical office buildings in Chongqing, and its effectiveness has been confirmed. A potential application of discovered knowledge is presented: early fault detection of anomalous electricity load profiles. The proposed framework can provide building managers with an efficient way to understand the characteristics of building electricity usage patterns and detect anomalies therein.

Electricity demand during pandemic times: The case of the COVID-19 in Spain

Electricity demand and its typical load pattern are usually affected by many endogenous and exogenous factors to which the generation system must accordingly respond through utility operators. Lockdown measures to prevent the spread of COVID-19 imposed by many countries have led to sudden changes in socioeconomic habits which have had direct effects on the electricity systems. Therefore, a detailed analysis of how confinement measures have modified the electricity consumption in Spain, one of the countries most affected by this pandemic, has been performed in this work. Its electricity consumption has decreased by 13.49% from March 14 to April 30, compared to the average value of five previous years. Daily power demand profiles, especially morning and evening peaks, have been modified at homes, hospitals, and in the total power demand. These changes generate a greater uncertainty for the System Operator when making demand forecasts, but production deviations have increased by only 0.1%, thanks to the presence of a diversified generation mix, which has been modified during this period, increasing the proportion of renewable sources and decreasing CO2 emissions.

Power consumption and energy efficiency of VRF system based on large scale monitoring virtual sensors

Space cooling energy consumption is a significant component of building energy consumption, and in recent years it has attracted much attention worldwide owing to its significantly increasing usage. The variable refrigerant flow (VRF) system is one common type of cooling equipment for buildings in China and is applied extensively to residential and office buildings. The performance of VRF systems significantly influences the cooling energy consumption of buildings. The system energy efficiency and electricity consumption are the main indicators employed to evaluate the performance of VRF systems. It is hard to obtain the actual energy efficiency and electricity consumption of VRF systems in buildings because of the high cost of the required complicated measurements. This study proposes a virtual sensor modeling method to determine the actual energy efficiency and electricity consumption of 344 VRF systems in residential buildings. Statistical and clustering analyses are conducted to determine the energy efficiency and electricity consumption to obtain distributions and typical operation load patterns of VRF systems in residential buildings in China. The main findings are as follows: the main range of the Seasonal Energy Efficiency Ratio (SEER) for the cooling season is from 2.9 to 4.4; the median SEER in the Hot Summer and Cold Winter zone is lower than in another climate zones; the longer cooling duration may lead to greater electricity consumption, and the electricity load for VRF systems electricity load is periodic for each day. The oversizing issue is common for VRF systems in the dataset, which also led to the lower energy efficiency of VRF systems. The high usage of VRF systems appeared from July 27th to August 26th. The findings provide recommendations for designing VRF systems in residential buildings.

Optimal structural design searching algorithm for cooling towers based on typical adverse wind load patterns

This investigation aims at typical adverse wind load patterns under tower group interference and overall structure optimization of large cooling towers considering multiple geometric parameters, focusing on potential unfavorable load patterns. Wind tunnel tests were performed on typical six-tower groups with various central distances, incoming flow directions and arrangement types, and external wind pressures were measured under interference conditions. Adverse wind load patterns are summarized with the help of interference criteria for three parameters: aerodynamic load, structure response and weighted internal force combination. Furthermore, overall structure optimization is achieved through the hybrid of grid search and gradient descent method, taking into account various possible wind loading distributions. Total 13 geometric parameters are adjusted to seek an optimized design that satisfy the requirement of structure strength, stability and economy under a particular wind load pattern. Crossover checks are then introduced to select a final optimized design scheme that is universally applicable to different adverse wind load patterns. The optimization procedure proposed in this study shows that adverse wind load patterns of grouped towers has distinct features comparing with wind load codes. It is necessary to consider the influence of multiple typical adverse wind load patterns for structural design, and the final optimization scheme should be determined through crossover checks to guarantee structure safety, stability and economy of cooling towers. • Typical adverse wind load patterns under complex six-tower group are proposed. • A hybrid optimization algorithm of the grid search and gradient descent is proposed. • Multi-parameters for cooling tower structure design are optimized. • Crossover check is used to ensure structural safety for multiple wind load patterns.

Meta-Heuristic Optimization Based Two-stage Residential Load Pattern Clustering Approach Considering Intracluster Compactness and Inter-cluster Separation

This article proposes a meta-heuristic optimization-based two-stage residential load pattern clustering (LPC) approach to address two main issues that exist in the most current LPC methods: 1) unreasonable typical load pattern (TLP) extraction; 2) a good clustering should achieve a good balance between the compactness and separation of the formed clusters. However, few clustering algorithms integrate both of these two aspects into the objective function of clustering for consideration. In the first stage, an adaptive density-based spatial clustering of applications with noise (DBSCAN) is proposed to automatically detect the uncommon load curves and obtain the TLP of each individual customer. In the second stage, LPC is formulated as an optimization problem in which clustering validity index (CVI) considering both compactness and separation is used as the objective function. Gravitational search algorithm (GSA) is adopted to solve this optimization problem. Four different CVIs are investigated to find the most appropriate one for LPC. A comparative case study using the real load data from 208 households from the U.K. verified the effectiveness of the proposed approach.

Clustering of electrical load patterns and time periods using uncertainty-based multi-level amplitude thresholding

This paper proposes a novel model to cluster similar load consumption patterns and identify time periods with similar consumption levels. The model represents the customer’s load pattern as an image and takes into account the load variation and uncertainty by using exponential intuitionistic fuzzy entropy. The advantage is that the proposed method can handle the uncertain nature of customer’s load, by adding a hesitation index to the membership and non-membership functions. A multi-level representation of the load patterns is then provided by creating specific bands for the load pattern amplitudes using intuitionistic fuzzy divergence-based thresholding. The typical load pattern is then determined for each customer. In order to reduce the number of features to represent each load pattern with respect to the time-domain data, the discrete wavelet transform is used to extract some spectral features. To cope with the data representation with fuzzy rules, the fuzzy c-means is implemented as the clustering algorithm. The proposed approach also identifies the time periods associated to different load pattern levels, providing useful hints for demand side management policies. The proposed method has been tested on ninety low voltage distribution grid customers, and its superior effectiveness with respect to the classical k-means algorithm has been represented by showing the better values obtained for a set of clustering validity indicators. The combination of load pattern clusters and time periods associated with the segmented load pattern amplitudes provides exploitable information for the efficient design and implementation of innovative energy services such as demand response for different customer categories.