Electricity Consumption Time Series 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.

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.

Electricity demand forecasting with hybrid classical statistical and machine learning algorithms: Case study of Ukraine

This article presents a novel hybrid approach using classic statistics and machine learning to forecast the national demand of electricity. As investment and operation of future energy systems require long-term electricity demand forecasts with hourly resolution, our mathematical model fills a gap in energy forecasting. The proposed methodology was constructed using hourly data from Ukraine’s electricity consumption ranging from 2013 to 2020. To this end, we analysed the underlying structure of the hourly, daily and yearly time series of electricity consumption. The long-term yearly trend is evaluated using macroeconomic regression analysis. The mid-term model integrates temperature and calendar regressors to describe the underlying structure, and combines ARIMA and LSTM “black-box” pattern-based approaches to describe the error term. The short-term model captures the hourly seasonality through calendar regressors and multiple ARMA models for the residual. Results show that the best forecasting model is composed by combining multiple regression models and a LSTM hybrid model for residual prediction. Our hybrid model is very effective at forecasting long-term electricity consumption on an hourly resolution. In two years of out-of-sample forecasts with 17520 timesteps, it is shown to be within 96.83% accuracy.

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.

Forecasting Electricity Demand by Neural Networks and Definition of Inputs by Multi-Criteria Analysis

The planning of efficient policies based on forecasting electricity demand is essential to guarantee the continuity of energy supply for consumers. Some techniques for forecasting electricity demand have used specific procedures to define input variables, which can be particular to each case study. However, the definition of independent and casual variables is still an issue to be explored. There is a lack of models that could help the selection of independent variables, based on correlate criteria and level of importance integrated with artificial networks, which could directly impact the forecasting quality. This work presents a model that integrates a multi-criteria approach which provides the selection of relevant independent variables and artificial neural networks to forecast the electricity demand in countries. It provides to consider the particularities of each application. To demonstrate the applicability of the model a time series of electricity consumption from a southern region of Brazil was used. The dependent inputs used by the neural networks were selected using a traditional method called Wrapper. As a result of this application, with the multi-criteria ELECTRE I method was possible to recognize temperature and average evaporation as explanatory variables. When the variables selected by the multi-criteria approach were included in the predictive models, were observed more consistent results together with artificial neural networks, better than the traditional linear models. The Radial Basis Function Networks and Extreme Learning Machines stood out as potential techniques to be used integrated with a multi-criteria method to better perform the forecasting.

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 decomposition study of the time series of electricity consumption forecasting errors

A decomposition study of the time series of electricity consumption forecasting errors

The study of the quality of multi-step time series forecasting

The work is devoted to the study of the quality of multistep forecasting of time series using the electricity consumption data for forecasting. Five models of multistep forecasting have been implemented, with their subsequent training and evaluation of the results obtained. The dataset is an upgraded minute-by-minute measurement of four years of electricity consumption. The dataset has been divided into training, validation, and test samples for training and testing models. The implementation is simplified by using the TensorFlow machine learning library, which allows us to conveniently process and present data; build and train neural networks. The TensorFlow functionality also provides standard metrics used to assess the accuracy of time series forecasting, which made it possible to evaluate the obtained models for forecasting the time series of electricity consumption and highlight the best ofthose considered according to the given indicators. The models are built in such a way that they can be used in studies of the quality of time series forecasting in various areas of human life. The problem of multistep forecasting for twenty fourhours ahead, considered in the paper, has not yet been solved for estimating electricity consumption. Theobtainedforecasting accuracy is comparable to recently published methods for estimating electricity consumption used in other conditions.At the same time, the forecasting accuracy of the constructed models has been improved in comparison with other methods.

A Bayesian Approach to Unsupervised, Non-Intrusive Load Disaggregation.

Estimating household energy use patterns and user consumption habits is a fundamental requirement for management and control techniques of demand response programs, leading to a growing interest in non-intrusive load disaggregation methods. In this work we propose a new methodology for disaggregating the electrical load of a household from low-frequency electrical consumption measurements obtained from a smart meter and contextual environmental information. The method proposed allows, with an unsupervised and non-intrusive approach, to separate loads into two components related to environmental conditions and occupants’ habits. We use a Bayesian approach, in which disaggregation is achieved by exploiting actual electrical load information to update the a priori estimate of user consumption habits, to obtain a probabilistic forecast with hourly resolution of the two components. We obtain a remarkably good accuracy for a benchmark dataset, higher than that obtained with other unsupervised methods and comparable to the results of supervised algorithms based on deep learning. The proposed procedure is of great application interest in that, from the knowledge of the time series of electricity consumption alone, it enables the identification of households from which it is possible to extract flexibility in energy demand and to realize the prediction of the respective load components.

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.

Matching intermittent electricity supply and demand with electricity storage - An optimization based on a time scale analysis

The rising share of Variable Renewable Energy Sources (VRES) in the electricity generation mix leads to new challenges for the whole energy system. It especially raises technological issues to handle variability and to match electricity load with supply at all times. This study introduces a new methodology to quantify the relevance of different electricity storage technologies, based on a time scale analysis. It additionally provides an understanding of how electricity storages work in combination to handle variable load and intermittent generation. First, we set up a simple model of variable production, fluctuating over a single time-scale. This analysis provides figures of merit for electricity storage and curtailment. Second, we simulate the collaboration and competition behavior of various storages with a dual time-scale signal. Then, results are compared with the optimization of an energy system with real variable electricity supply and consumption time-series. We eventually highlight the trade-off mechanisms between the storage efficiency and its investment cost.

Analysis of Electricity Consumption in Poland Using Prediction Models and Neural Networks

The challenges of the modern world require transformations in the energy market towards the possible reduction of consumption and greater use of renewable sources. The conducted research of consumers of this market confirms that the behaviour in the field of increased use of renewable energy is burdened with cognitive errors and motivational factors, which makes it difficult to conduct quantitative research. Electricity demand forecasting can be modelled using selected quantitative methods. In this way, not so much the behaviour, but the result of the consumer’s behaviour is predicted. The research presented in the article has been divided into two parts. The aim of the first one is to study the prospects of a greater share of renewable sources in obtaining energy in Poland, based on the attitudes and opinions of consumers on the retail energy market, legal regulations and the energy balance. The aim of the second part is to build forecasts of daily, weekly, monthly and quarterly electricity consumption in Poland, including the prediction of the RES share, using selected machine and deep learning methods. The analyses used the time series of daily electricity consumption in Poland from 2015–2021; the ENTSO-E data was obtained from the cire.pl website. Depending on the adopted forecast horizon, the forecasting method with the lowest MAPE error was exponential smoothing, SARIMA and NNETAR. An evolution of energy consumers’ attitudes towards pro-ecological and pro-social sensitivity and understanding of the importance of RES for the economy was also observed.

Symposium Title: Cognitive Resources in Affective States and After Brain Damage

The conditions for economic motivation for cost-effective use of resources are a prime requirement if an organization needs to implement energy-saving technologies, regardless of its purpose and type of activity. Energy-saving methods must be applied both at the technical level, and the system (management) level. The basis of energy saving is the systematic implementation of a set of technical and technological measures, which should be preceded by the optimization of organizations’ energy consumption at the system level. The chapter uses a rank analysis to show the efficiency of approximating the electricity consumption at the units of mining enterprises. The interval estimation procedure allowed for the identification and grading the mining enterprise units, revealing those with the abnormal electricity consumption. The trend of time series of relative electricity consumption for each facility is calculated. The chapter uses the results of calculations containing the health index figures for electricity consumption to establish facilities that require a priority in-depth energy inspection. This chapter focuses on the development of a methodology for timely identification of priority units for an in-depth energy survey for the implementation of energy saving projects and increasing the efficiency of electricity use in mining enterprises as objects of technocenosis.

Online hierarchical forecasting for power consumption data

This paper proposes a three-step approach to forecasting time series of electricity consumption at different levels of household aggregation. These series are linked by hierarchical constraints—global consumption is the sum of regional consumption, for example. First, benchmark forecasts are generated for all series using generalized additive models. Second, for each series, the aggregation algorithm ML-Poly, introduced by Gaillard, Stoltz, and van Erven in 2014, finds an optimal linear combination of the benchmarks. Finally, the forecasts are projected onto a coherent subspace to ensure that the final forecasts satisfy the hierarchical constraints. By minimizing a regret criterion, we show that the aggregation and projection steps improve the root mean square error of the forecasts. Our approach is tested on household electricity consumption data; experimental results suggest that successive aggregation and projection steps improve the benchmark forecasts at different levels of household aggregation.

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.

The nexus of electricity and economic growth in major economies: The United States-India-China triangle

A recently developed bootstrap autoregressive-distributed lag test with structural breaks has been used to test the cointegration and causality regarding major economies worldwide, in order to investigate the relationship between electricity and economic growth during the period 1971–2014. Although a long-run cointegrating relationship between the time series of real GDP and electricity consumption per capita cannot be sufficiently detected, the results of this research show the contingency of causality. In India, the past histories of electricity consumption serve as a predictive indicator influencing the present states of economic growth. Positive and negative feedback exists in the United States and China, respectively. The results of the growth hypothesis support the government in India budgeting an enormous amount of money for its infrastructural development in the fiscal year 2017–2018. The government in the United States continues to investigate modern methods of developing the supply of electricity, such as renewable energy technologies. After the economic growth in China, Chinese citizens live in better conditions and garner a higher level of culture. Thus, they may pursue lower-energy-consuming products to strive for a better environment. Based on empirical analysis, certain policy and managerial implications have been addressed by decision-makers at the macroeconomic and microeconomic levels.

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

FULL-TARGETED ASPECTS OF MONITORING OF ELECTRICITY LEVELS AT ENTERPRISES OF THE INDIVIDUAL DEPARTMENT OF THE KRIIVORIZHOGY REGION

Purpose. Conduct a cost-target analysis as part of monitoring electricity consumption levels and develop recommendations for a rational strategy for consuming electricity from mining enterprises with underground mining of iron ore.Methodology. The study is based on the use of laws, regularities and categorical apparatus. In the process of scientific research, general scientific methods of research (comparison, generalization, analogy method, structural analysis and synthesis), methods of logical-theoretical analysis, special economic-mathematical methods were used.Findings. The indicators of electricity consumption at iron ore enterprises of the Krivoy Rog region are analyzed. A comparative analysis of the planned and actual energy consumption is carried out. The trend components of electricity consumption indicators at iron ore enterprises of the Krivoy Rog region are highlighted. Dynamics of structural transformation of unit cost of electric energy production at iron ore enterprises of the Krivoy Rog region is determined.Originality. When forming the cost-target component of monitoring the energy consumption levels, it is suggested to use the index methodology in combination with trend models, which will allow to determine the forecast of electricity consumption and realize rational management of power consumption. An index system of structural transformation of the unit cost of electric energy production at mining enterprises of the Krivoy Rog region is proposed.Practical value. Dynamics analysis of the structural transformation of the unit cost of electric power consumption of iron ore enterprises of the Krivoy Rog region is carried out. The system of indices of the unit cost of electricity consumption is defined: the index of the average unit cost of electricity consumption of variable composition; index of unit cost of power consumption of fixed composition; the index of the average unit cost of power consumption of structural shifts. The deterministic component of the time series of electricity consumption for the iron ore enterprises of the Krivoy Rog region was singled out and analyzed. The obtained trend models give grounds for determining the forecast of electricity consumption levels with an appropriate level of reliability. The indicators of electricity consumption levels provide the formation of an information-analytical component of the relevant monitoring. The carried out researches allow to formulate the strategy of electricity consumption at the enterprises of iron ore industry of the Krivoy Rog region. The problems highlighted in the article are closely related to the scientific and practical tasks of rational use and distribution of available energy resources.