Electricity Consumption Time Research Articles

Generating Synthetic Electricity Load Time Series at District Scale Using Probabilistic Forecasts

Thanks to various European directives, individuals are empowered to share and trade electricity within Renewable Energy Communities, enhancing the operational efficiency of local energy systems. The digital transformation of the energy market enables the integration of decentralized energy resources using cloud computing, the Internet of Things, and artificial intelligence. In order to assess the feasibility of new business models based on data-driven solutions, various electricity consumption time series are necessary at this level of aggregation. Since these are currently not yet available in sufficient quality and quantity, and due to data privacy reasons, synthetic time series are essential in the strategic planning of smart grid energy systems. By enabling the simulation of diverse scenarios, they facilitate the integration of new technologies and the development of effective demand response strategies. Moreover, they provide valuable data for assessing novel load forecasting methodologies that are essential to manage energy efficiently and to ensure grid stability. Therefore, this research proposes a methodology to synthesize electricity consumption time series by applying the Box–Jenkins method, an intelligent sampling technique for data augmentation and a probabilistic forecast model. This novel approach emulates the stochastic nature of electricity consumption time series and synthesizes realistic ones of Renewable Energy Communities concerning seasonal as well as short-term variations and stochasticity. Comparing autocorrelations, distributions of values, and principle components of daily sequences between real and synthetic time series, the results exhibit nearly identical characteristics to the original data and, thus, are usable in designing and studying efficient smart grid systems.

Forecasting electricity demand of municipalities through artificial neural networks and metered supply point classification

This study develops a methodology to characterise and forecast large consumers’ electricity demand, particularly municipalities, with hundreds of different metered supply points based on the previous characterisation of facilities’ consumption. Demand forecasting allows consumers to improve their participation in electricity markets and manage their electricity consumption. The method considers a classification by different types of metered supply points combined with artificial neural networks to obtain hourly forecasts using well-known parameters such as day types, hourly temperature, the last hour of electricity consumption, and sunrise and sunset time. We apply the methodology to the municipality of Valencia using over five hundred hourly load profiles for a year during 2017 and 2018. Our results present aggregated forecasts with a maximum Mean Absolute Percentage Error of 3.8% per day, outperforming the same forecast without classifying Metered Supply Points. We conclude that a correct electricity demand forecast for a consumer with different types of consumption does not need submetering, but characterising Metered Supply Points is an option with lower costs that allows for better predictions.

House and socio-demographic features vs. electricity consumption time series in main heating mode classification

Demand-side flexibility is crucial for integrating variable renewable energy sources cost-effectively. Home heating systems determine the potential for flexibility in individual households. We examine different approaches to classify heating systems in Finland and find that using hourly electricity consumption time series is more effective than house and socio-demographic features. Classification based on electricity consumption data achieves higher precision (0.62) and recall (0.64) than house and socio-demographic features (0.41 and 0.43, respectively). Therefore, the availability of electricity consumption time series data should be considered from a competition policy perspective due to its value in estimating flexibility potential.

Pre-forecast modeling of airport electricity consumption time series

Pre-forecast modeling of airport electricity consumption time series

Joint Optimization of Resource Utilization, Latency and UAV Trajectory in the Power Information Acquisition System

In order to reduce the peak–valley difference of the power grid load, reasonably arrange users’ electricity consumption time and realize the intelligent management of the power grid, we construct a user electricity consumption information acquisition system based on unmanned aerial vehicles (UAVs) by using a sensor network. In order to improve the service quality of the system and reduce the system delay, this paper comprehensively considers the factors that affect the user’s electricity consumption information collection system, such as the UAV trajectory, the unloading decision of the data receiving point and so on. Therefore, this paper puts forward an effective iterative optimization algorithm for joint UAV trajectory and unloading decisions based on a deep Q network (DQN), in order to obtain the optimal UAV trajectory and unloading decision design, acquire the optimal solution to minimize the time delay of the monitoring system and maximize the service quality of the user electricity information collection system, thus ensuring the stable operation of the user electricity information collection system. In this paper, different complexity algorithms are used to solve this problem. Compared with the greedy algorithm, the proposed algorithm, CDQN, improves the system service quality by approximately 2% and reduces the system delay by approximately 16%, so that the user’s electricity consumption information can be analyzed and processed faster.

Multiple Novel Decomposition Techniques for Time Series Forecasting: Application to Monthly Forecasting of Electricity Consumption in Pakistan

In today’s modern world, monthly forecasts of electricity consumption are vital in planning the generation and distribution of energy utilities. However, the properties of these time series are so complex that they are difficult to model directly. Thus, this study provides a comprehensive analysis of forecasting monthly electricity consumption by comparing several decomposition techniques followed by various time series models. To this end, first, we decompose the electricity consumption time series into three new subseries: the long-term trend series, the seasonal series, and the stochastic series, using the three different proposed decomposition methods. Second, to forecast each subseries with various popular time series models, all their possible combinations are considered. Finally, the forecast results of each subseries are summed up to obtain the final forecast results. The proposed modeling and forecasting framework is applied to data on Pakistan’s monthly electricity consumption from January 1990 to June 2020. The one-month-ahead out-of-sample forecast results (descriptive, statistical test, and graphical analysis) for the considered data suggest that the proposed methodology gives a highly accurate and efficient gain. It is also shown that the proposed decomposition methods outperform the benchmark ones and increase the performance of final model forecasts. In addition, the final forecasting models produce the lowest mean error, performing significantly better than those reported in the literature. Finally, we believe that the framework proposed for modeling and forecasting can also be used to solve other forecasting problems in the real world that have similar features.

Scenario generation of residential electricity consumption through sampling of historical data

The low-voltage grid (LVG) needs to be reinforced to handle the increased load due to the transition towards renewable energy. Doing this optimally requires knowledge of typical currents and voltages throughout the grid, which are unknown. They can be calculated from the grid layout and electricity consumption time series of each consumer, but for many consumers this time series is unknown. To alleviate this problem, we have developed two techniques to generate accurate and realistic daylong electricity consumption time series (scenarios) for a given consumer. Both techniques generate scenarios by sampling from historical consumption measurements of a limited set of consumers, considering available information about consumers (e.g., total yearly consumption) and days (e.g., weather). The first technique uses expert knowledge to define this sampling procedure, whereas the second learns it automatically using machine learning. The quality of the generated scenarios is evaluated by estimating how well the distributions of predicted and observed time series match, conditional on the available information. The data-driven technique performs better than the expert-based technique and, contrary to the latter, can easily be applied to datasets with different attributes without requiring any domain knowledge. Both proposed techniques outperform random sampling (the prevalent approach in existing LVG studies) and standard load profiles (commonly used by distribution system operators (DSOs)). Most of the improvement is obtained by including consumer attributes, whereas daily attributes lead to little or no performance improvement. The proposed techniques can help DSOs estimate the state of the LVG more accurately, thereby reducing investment costs.

A Trolley Wire De-Icing System

This paper presents a dedicated system for de-icing trolley wires. The proposed issue is appropriately under the protection of intellectual property for solving as described in patent no.B1 230665 PL in the Patent Office of the Republic of Poland. In the solution presented herein, de-icing is achieved mainly by electrodynamic force excitation and secondarily as a result of heating. Because ice is removed primarily through vibrations, it avoids a large consumption of electricity associated with the high specific heat of water. More importantly, operation is nearly simultaneously applied to the total distance of the trolley power grid between two substations. For this reason, it requires less electricity consumption and less time to apply than in heating, mechanical, or chemical methods.

Stackelberg-Game-Based Demand Response for Voltage Regulation in Distribution Network with High Penetration of Electric Vehicles

With the development of the economy, electricity demand continues to increase, and the time for electricity consumption is concentrated, which leads to increasing pressure on the voltage regulation of the distribution network. For example, a large number of electric vehicles charging during a low-price period may cause the problem of under-voltage of the distribution network. On the other hand, the penetration of distributed power generation of renewable energy may cause over-voltage problems in the distribution network. This study proposes a Stackelberg game model between the distribution system operator and the load aggregator. In the Stackelberg game model, the distribution system operator affects the users’ electricity consumption time by issuing subsidies to decrease the frequency of voltage violations. As the representative of users, the load aggregator helps the users schedule the demand during the subsidized period to maximize profits. Case studies are carried out on the IEEE 33-bus power distribution system. The results show that the time of the subsidy can be optimized based on the Stackelberg game model. Both the distribution system operator and the load aggregator can obtain the optimal economic profits and then comprehensively improve the operating reliability and economy of the power distribution system.

Comparison of different methods of spatial disaggregation of electricity generation and consumption time series

Energy system models involve various input data sets representing the generation, consumption and transport infrastructure of electricity. Especially energy system models with a focus on the transmission grid require time series of electricity feed-in and consumption in a high spatial resolution. In general, there are two approaches to obtain regionalized time series: top-down and bottom-up. In many cases, both methodologies may be combined to aggregate or disaggregate input data. Furthermore, there exist various approaches to assign regionalized feed-in of renewable energy sources and electrical load to the model’s grid connection points. The variety in the regionalization process leads to significant differences on a regional scope, even if global values are the same.We develop a Methodology to compare regionalization techniques of input data for photovoltaics, wind and electrical load between various models as well as data assignment techniques to the power grid nodes. We further define two invariants to evaluate the outcome of the regionalization process at the NUTS 3 level, one invariant for the annual profiles and one for the installed capacities. This Methodology enabled us to compare different regionalization and assignment workflows using simple parameters, without explicit knowledge of grid topology. Our results show that the resolution of the input data and the use of a top-down or a bottom-up approach are the most determinant factors in the regionalization process.

NSGA-II multi-objective optimization regional electricity price model for electric vehicle charging based on travel law

Electric vehicle is the key technology to tackle the issue of energy and emissions, a great number of EVs connected to the grid will negatively impact on the economic operation of the grid. Based on the statistics of electric vehicle travel rules, this paper used NSGA-II multi-objective optimization algorithm to establish a regional electricity price model. The goal is to reduce the peak load through electricity consumption time and ensure the interests of operators, The effectiveness of the algorithm is verified by an example of a certain area in Shandong Province. The algorithm did not change the space–time distribution of existing charging stations, and only used the regional electricity price to adjust the charging load of electric vehicles, which has high practical significance.

Towards the Establishment of a Robust Load Forecasting Mechanism in Tanzania Grid: A Study to Investigate Effect of Air Temperature and Daytime on Electricity Consumption in Residential Buildings

- The current phenomenon of electric power management is towards the adoption of smart grid technologies to achieve efficient utility management processes such as transmission and distribution. Electric load forecasting has become an important aspect of smart grid technologies due to its capability of anticipating the power demand of a particular domain. The effective design of any load forecasting mechanism requires a critical investigation of electricity consumption determinants, following the diversification of social, economic, meteorological, and demographic grounds. Many research works have attempted to investigate the effect of temperature and daytime on electricity usage, targeting a specific country. The works have reported the existence of differing degrees of causality based on a particular country under investigation. The variation in findings from different research works has been a motivation to establish the study to examine the impact of air temperature and day time on load consumption in Tanzania. Four-years load and weather data have been collected from Tanzania Electric Supply Company (TANESCO) and Tanzania Meteorological Agency (TMA) respectively. The k-mean algorithm is used to detect outliers and missing values in the load dataset before further processing. Furthermore, the Shapiro-Wilk normality test method is applied to identify data distribution patterns which in turn leads us to the correct choice for Spearman’s rank correlation method. Results indicate that there is no linear relationship between electricity consumption and air temperature in residential buildings. Finally, findings indicate the existence of a strong causality degree between electricity consumption and day time in Tanzania.

Non-Technical Loss Detection in Power Grid Using Information Retrieval Approaches: A Comparative Study

Non-technical loss (NTL) detection is a persistent challenge for Distribution System Operators. Data-driven solutions have been widely used nowadays to analyze customers’ energy consumption and to identify suspicious fraud patterns for a posterior on-field inspection. However, the usage of such techniques, in particular the current deep learning methods, is not trivial and requires special attention to tackle imbalanced-class and overfitting issues. In this paper, we propose a new non-technical loss detection framework, which combines the effectiveness of convolutional neural network feature extractors with the efficiency of the Information Retrieval paradigm. In our solution, state-of-the-art pre-trained convolution neural networks (CNNs) extract deep features from electricity consumption time series represented as images. Next, these deep features are encoded into textual signatures and indexed using off-the-shelf solutions for posterior fraud searching. With this framework, the user can search for a specific fraud pattern in the utility database without having to train any classifier. The experiments performed in a real dataset provided by CPFL Energia, one of the largest electric utilities in Brazil, presented promising results both in terms of effectiveness and efficiency for the detection of fraudulent customers. In the conducted comparative study, we evaluate different time series image representations and CNN feature extraction approaches with regard to NTL detection results. Experimental results demonstrate that the combination of the Recurrence Plot image representation with the VGG16 CNN presented the best performance in terms of both effectiveness and efficiency.

A Single Scalable LSTM Model for Short-Term Forecasting of Massive Electricity Time Series

Most electricity systems worldwide are deploying advanced metering infrastructures to collect relevant operational data. In particular, smart meters allow tracking electricity load consumption at a very disaggregated level and at high frequency rates. This data opens the possibility of developing new forecasting models with a potential positive impact on electricity systems. We present a general methodology that can process and forecast many smart-meter time series. Instead of using traditional and univariate approaches, we develop a single but complex recurrent neural-network model with long short-term memory that can capture individual consumption patterns and consumptions from different households. The resulting model can accurately predict future loads (short-term) of individual consumers, even if these were not included in the original training set. This entails a great potential for large-scale applications as once the single network is trained, accurate individual forecast for new consumers can be obtained at almost no computational cost. The proposed model is tested under a large set of numerical experiments by using a real-world dataset with thousands of disaggregated electricity consumption time series. Furthermore, we explore how geo-demographic segmentation of consumers may impact the forecasting accuracy of the model.

Short-term Time Series Data Prediction of Power Consumption Based on Deep Neural Network

Under the background of the rapid development of Internet technology and the popularity of smart grids, the analysis and prediction of short-term time series data of users’ power consumption has important guiding significance for grid planning, management decision of economic sector and optimization and allocation of power resources. Considering that the traditional statistical-based time series analysis method is weak in generality and can not handle the complex linear problem in prediction, the long-term dependence of the ordinary cyclic neural network model is insufficient, and the time series data has multidimensional problems, a deep neural network is proposed. The PCA-LSTM model is used for time series data prediction. The model firstly uses the PCA (principal component analysis) method to reduce the dimensionality of the electricity consumption time series data, optimizes the number of input variables, and inputs the data into the long- and short-term memory network LSTM for training prediction. The experimental results show that the LSTM network prediction based on PCA improves the accuracy of short-term time series data prediction, and also improves the convergence speed of LSTM network. It proves that the method has better prediction performance and versatility.

Electricity Consumption Behaviour Analysis Based on Time Sequence Clustering

Accurately grasping electricity consumption behaviour and trend in different period can provide decision support for electric resource scheduling. It is necessary to analyze the electricity consumption time sequence. To serve the purpose, a clustering method called VDSI (Variance of Difference between two Sequence Items) is presented by analyzing the typical daily electricity consumption. In the method, time sequence of electricity consumption is split into sub-sequences based on a predefined time window. Firstly, any two time sub-sequences are converted to a new one, in which any item is the difference between items of the two subsequences. Then, the variance is used to measure the distance of the two sub-sequences. Finally, hierarchical clustering algorithm is used to cluster sequences in different time window. By comparing with traditional sequence clustering, the VDSI can obtain more accurate clustering results.

Estimation of total electricity consumption curves by sampling in a finite population when some trajectories are partially unobserved

AbstractMillions of smart meters that are able to collect individual load curves, that is, electricity consumption time series, of residential and business customers at fine scale time grids are now deployed by electricity companies all around the world. It may be complex and costly to transmit and exploit such a large quantity of information, therefore it can be relevant to use survey sampling techniques to estimate mean load curves of specific groups of customers. Data collection, like every mass process, may undergo technical problems at every point of the metering and collection chain resulting in missing values. We consider imputation approaches (linear interpolation, kernel smoothing, nearest neighbours, principal analysis by conditional estimation) that take advantage of the specificities of the data, that is to say the strong relation between the consumption at different instants of time. The performances of these techniques are compared on a real example of Irish electricity load curves under various scenarios of missing data. A general variance approximation of total estimators is also given which encompasses nearest neighbours, kernel smoothers imputation and linear imputation methods. The Canadian Journal of Statistics 47: 65–89; 2019 © 2018 Statistical Society of Canada

Impacts of wireless charging lanes on travel time and energy consumption in a two-lane road system

In this paper, we propose a method to compare different energy consumption models and design a strategy to study the quantitative effects of wireless charging lane (WCL) on each electric vehicle’s (EV’s) link travel time. We utilize the modified energy consumption model and strategy to explore electric vehicle’s electricity consumption and link travel time in a two-lane system with a WCL. The numerical results show that EVs’ charging behavior on WCL will cause the drivers to execute the lane-changing maneuvers frequently and that the WCL has prominent impacts on EV’s energy consumption and travel time, i.e., the capacity drops by 8%–17% while the EV’s energy consumption increases by 3%–14% in the two-lane road system.

The economical modelling of a distribution system for electricity supply chain

We propose an integrated distribution system model for electricity supply chain. The distribution system consists of a power plant, a transmission substation, a distribution substation and multi-customers. The electricity generated by the power plant is transmitted to the submission and distribution substations, and finally consumed by the customers. We apply the inventory theory to the distribution system model and assume the electricity demand is normally distributed. The objective is to minimize the joint total expected cost (JTEC) incurred by the power plant, transmission substation, distribution substation and multi-customers. The JTEC includes the transmission and the distribution costs, the blackout cost, the energy storage cost, setup/ordering cost, and the production cost. We develop a procedure to derive the optimal solution and the decision variables using differential calculus. The model is tested with an artificially generated data. The results in Fig. 5 show that as JTEC increases, the parameters of the average and standard deviation of electricity demand, power supply rate, length of lead time, capacities of power plant and transmission substation increase. The cost decreases as the blackout ratio and distribution substation capacity increase. The cost is insensitive to the changing electricity consumption time.

Multivariate exploration of non-intrusive load monitoring via spatiotemporal pattern network

Non-intrusive load monitoring (NILM) of electrical demand for the purpose of identifying load components has thus far mostly been studied using univariate data, e.g., using only whole building electricity consumption time series to identify a certain type of end-use such as lighting load. However, using additional variables in the form of multivariate time series data may provide more information in terms of extracting distinguishable features in the context of energy disaggregation. In this work, a novel probabilistic graphical modeling approach, namely the spatiotemporal pattern network (STPN) is proposed for energy disaggregation using multivariate time-series data. The STPN framework is shown to be capable of handling diverse types of multivariate time-series to improve the energy disaggregation performance. The technique outperforms the state of the art factorial hidden Markov models (FHMM) and combinatorial optimization (CO) techniques in multiple real-life test cases. Furthermore, based on two homes’ aggregate electric consumption data, a similarity metric is defined for the energy disaggregation of one home using a trained model based on the other home (i.e., out-of-sample case). The proposed similarity metric allows us to enhance scalability via learning supervised models for a few homes and deploying such models to many other similar but unmodeled homes with significantly high disaggregation accuracy.