Electricity Data Research Articles

Analysing electricity demand in neighbourhoods with electricity generation from solar power systems: A case study of a large housing cooperative in Norway

An energy management system can be introduced on a neighbourhood level, to achieve energy goals such as increased self-consumption of locally produced energy. In this case-study, electricity generation from photovoltaic (PV) systems is simulated at Risvollan housing cooperative, a large housing cooperative in Norway. The electricity generation from PV systems of different orientations and capacities are analysed with the electricity load. Key performance indicators (KPIs) such as self-generation, self-consumption and generation multiple are described, based on hourly values. The electricity generation from the south-oriented building façade PV systems are about 5-6% higher than for the east-west oriented rooftop PV systems on an annual basis, since the façade PV systems generate more electricity in the spring and autumn. The self-consumption factor is the most important KPI in Norway, due to the national tariff structure. For the total housing cooperative, a PV capacity of about 1,000 kWp seem suitable, giving a self-consumption factor of 97% for a rooftop system, based on 2018 electricity and climate data. From the perspective of the housing cooperative, it is financial beneficial to aggregate electricity loads for common areas and apartments, since a higher share of the electricity can be used by the cooperative. For this to be possible, also housing cooperatives with PV must be facilitated for in the prosumer agreement. Comparing a single 1,100 kWp PV system providing electricity to the total cooperative with 22 PV systems of 50 kWp behind 22 garage meters, the self-consumption factor decreases from 95% to average 14%, resulting in a 41% lower financial value for the PV electricity.

Effect of increased building-integrated renewable energy on building energy portfolio and energy flows in an urban district of Korea

Renewable energy sources such as solar panels with energy storage, ground-source heat pumps, and fuel cells are increasingly being integrated into buildings to improve their sustainability. This paper investigates, for an urban area, the effects of building-integrated renewable energy on the building energy portfolio and electricity & gas flows from the grid. In this study, 403 apartment buildings and 269 non-residential buildings (with a floor area over 500 m2) in a district of Seoul, Korea, are assumed to integrate renewable energy sources to satisfy the renewable energy requirements of 10% and 30% of the total energy demand, respectively. The optimal renewable energy system for each building has been obtained via Linear Programming. The optimization has been carried out based on the hourly electricity, heating, and cooling loads of every building over a 1-year period. The energy loads, in turn, have been derived from the monthly electricity and gas usage data for every building. The renewable energy portfolio for the apartment buildings consists of photovoltaics, fuel cells, and solar water heaters. About 74% of the renewable energy for apartment buildings is provided by photovoltaics. The renewable energy portfolio for the non-residential buildings consists of ground-source heat pumps and photovoltaics. About 71% of the renewable energy is provided by the ground-source heat pumps. Consequently, total electricity and gas supply from the grid to all of the buildings in the district decrease by 17% and 3%, respectively. The photovoltaics induce a large variation in power flow from the grid during the daytime (i.e., the so-called Duck curve). The annual peak power occurs in the winter mornings because electricity powered heating by ground-source heat pumps replaces gas heating, and maximum heat demand occurs in winter mornings.

Measurement and verification of energy conservation measures using whole-building electricity data from four identical office towers

Reliable measurement and verification (M&V) of the energy savings associated with energy conservation measures (ECMs) is critical for financial settlement of utility incentives and energy performance contracts. With the proliferation of advanced metering infrastructure, there is increasing interest in using whole-building data for M&V, as a cost-effective alternative to sub-metering or installing equipment-specific data loggers. Commonly observed protocols provide a framework for regression-based M&V analysis of whole-building data. This paper leverages seven years of whole-building electricity data from four geometrically-identical multi-storey multi-tenant co-located office towers, which underwent a series of very similar ECMs, to explore the application and validity of straightforward, regression-based models. Results showed the models met the criteria for goodness of fit and statistical uncertainty (R2 above 0.75; coefficient of variation of the root-mean-square error, CV(RMSE) below 25%; normalized mean bias error, NMBE below 0.005%; standard error in the unstandardized regression coefficient estimating an ECM effect less than 50% of the regression coefficient itself). Further, these regression models were (in the large majority of cases) able to differentiate between multiple ECMs applied sequentially, and that, in some cases, effects as low as 5% were reported. Effect estimates of similar ECMs were similar across the buildings, but were generally substantially larger than the pre-retrofit engineering predictions of savings. ECM effect estimates using monthly and hourly data with equivalent regression formulations gave very similar estimates, differences in coefficients ranged between approximately 0–15%, with a mean of 7%. Estimates of ECM effects made using the full timespan of data were more sensitive, and generally larger, than estimates using only a single year of pre- and post-ECM data. However, visually identifying ECM effects in time-series data was challenging, which highlights the many sources of error in such an analysis.

Risk-based Cooperative Scheduling of Demand Response and Electric Vehicle Aggregators

This paper proposes a new cooperative scheduling framework for demand response aggregators (DRAs) and electric vehicle aggregators (EVAs) in a day-ahead market. The proposed model implements the information-gap decision theory (IGDT) to optimize the scheduling problem of the aggregators, which guarantees obtaining the predetermined profit by the aggregators. In the proposed model, the driving pattern of electric vehicle owners and the uncertainty of day-ahead prices are simulated via scenario-based and a bi-level IGDT based methods, respectively. The DR aggregator provides DR from two demand side management programs including time-of-use (TOU) and reward-based DR. Then, the obtained DR is offered into day-ahead markets. Furthermore, the EVA not only meet the EV owners’ demand economically, but also participates in the day-ahead mark while willing to set DR contracts with the DR aggregator. The objective function is to maximize the total profit of DR and EV aggregators perusing two different strategies to face with price uncertainty, i.e., risk-seeker strategy and risk-averse strategy. The proposed plan is formulated in a risk-based approach and its validity is evaluated on a case study with realistic data of electricity markets.

A Review Paper on Existing Security Algorithm to Find Out Issues for Achieving the Confidentiality and Integrity of Metering Data in Smart Grid

The advancement of electric grid into smart grid has become possible by integrating communication network with the component of power system for free disturbance and effective power distribution. Advanced metering infra-structure (AMI) is used for measurement, collection, analysis and the electricity consumption of the customer. This AMI has variety of sensors for the consumption of electricity usage, smart meter, data concentrators, various server and different communication network. The implementation of net-work for making information available all the time introduces the cyber security concern on metering data of the customer. The modification or adulteration on metering data may lead to Financial loss during billing, false pre-diction of required energy and physical damage to the equipment present in the entire infrastructure. Hence it become essential to ensure confidentiality and integrity of metering data. In this paper various security algorithm has been surveyed to understand the factor for achieving the confidentiality and integrity of smart metering data. At last comparative work and future scope is discussed to identify factors and their importance for cyber security on metering data of the customer has been identified.

Who is sensitive to DSM? Understanding the determinants of the shape of electricity load curves and demand shifting: Socio-demographic characteristics, appliance use and attitudes

To date, research on demand side management has mostly focused on the determinants of electricity consumption and stated preference experiments to understand social acceptability. Further experimental research is needed to identify the determinants for demand response schemes. This paper contributes to addressing this gap by making use of data from a randomised control trial which contains 15 months of smart meter electricity data combined with household characteristics and differences in incentives to shift their electricity use between 11am and 3pm. Cluster analysis performed on electricity data identified three distinct electricity daily load profiles. Each cluster was then linked to household characteristics by means of a multinomial logistic regression to identify the determinants of the load curves' shapes. Findings show that occupancy presence at home, age and appliance ownership were strong predictors. Finally, this paper is among the first to provide experimental evidence on the determinants of load shifting. We find that households with head aged above 65, households who belong to the cluster exhibiting a load profile characterised by a relatively high peak at noon and a low peak in the evening, and those who received money incentives were more likely to shift electricity use towards middle of the day (11am-3pm).

Benefit assessment of battery plus solar for customers and the grid

A method that can assess the benefit for both the customers and the distribution grid when household or community batteries are installed without central control is presented. An agent-based model is used where the household assets' characteristics and behaviours are modelled and linked to a network model. Electricity data from dwellings on one street in Townsville, Australia, was used to populate the models, and simulations were run under three battery scenarios. The scenarios considered were a) “Business as usual” when no battery storage is installed, b) individual batteries are installed at each household, c) a community battery that would supply all the households is installed. Customer benefits are calculated from the operational costs savings using two types of tariffs available in Queensland. The network assets’ health is assessed considering load, current and voltage levels at the distribution transformer. These simulations lead to a better informed decision for the customer, and give the utility insight into how such technologies might impact their assets.

Estimating country-specific space heating threshold temperatures from national gas and electricity consumption data

Space heating in buildings is becoming a key element of sector-coupled energy system research. Data availability limits efforts to model the buildings sector, because heat consumption is not directly metered in most countries. Space heating is often related to weather through the proxy of heating degree-days using a specific heating threshold temperature, but methods vary between studies. This study estimates country-specific heating threshold temperatures using widely and publicly available consumption and weather data. This allows for national climate and culture-specific human behaviour to be captured in energy systems modelling. National electricity and gas consumption data are related to degree-days through linear models, and Akaike’s Information Criteria is used to define the summer season in each country, when space heating is not required. We find that the heating threshold temperatures computed using daily, weekly and monthly aggregated consumption data are statistically indifferent. In general, threshold temperatures for gas heating centre around 15.0 ± 1.7 ∘C (daily averaged temperature), while heating by electricity averages to 13.4 ± 2.4 ∘C. We find no evidence of space heating during June, July and August, even if heating degree-days are present.

Implications of energy and CO2 emission changes in Japan and Germany after the Fukushima accident

Following the March 2011 nuclear power plant accident in Fukushima, Japan, nuclear power production declined sharply in that country as well as Germany. Despite widespread media coverage of CO2 emission increases in the first few years afterward, subsequent energy and emission changes and their implications are not well-studied. Here we analyze energy, electricity, and CO2 emissions data for both countries through 2017. We also quantify the human health and CO2 implications of two simple yet illuminating scenarios: What if both countries had reduced fossil fuel power output instead of nuclear? And what if the US and the rest of Europe eliminate their remaining nuclear power? We find that emissions increased after Fukushima until 2013 but decreased thereafter due to record-high renewable energy production and lower total energy use. However our “what if” scenarios demonstrate that these two countries could have prevented 28,000 air pollution-induced deaths and 2400 MtCO2 emissions between 2011 and 2017. Germany can still prevent 16,000 deaths and 1100 MtCO2 emissions by 2035 by reducing coal instead of eliminating nuclear as planned. If the US and the rest of Europe follow Germany's example they could lose the chance to prevent over 200,000 deaths and 14,000 MtCO2 emissions by 2035.

A novel hybrid model based on neural network and multi-objective optimization for effective load forecast

In recent years, increased attention has been paid by the researchers to predict accurate and stable load due to its effect on the economy and need for proper management of power systems. However, most of the previous research focused only on either reducing load forecast error or enhancing the stability, very few studies focused on these two issues simultaneously. Introducing a forecasting model to solve both independent objectives at the same time is a challenging task due to the complex behavior of the load pattern.Therefore, to achieve two objectives simultaneously, we propose a novel multi-objective algorithm (MOFTL) based on Follow The Leader algorithm. The effectiveness of MOFTL has been shown by comparing the results with three newly presented MOWCA, MOPSO and NSGA-II multi-objective algorithms. Moreover, to validate the performance of MOFTL, we have combined MOFTL with neural network termed as MOFTL-ANN to solve the problem of electricity load forecasting. The proposed hybrid model outperforms baseline models over two real-world electricity data sets namely England region and ERCOT region. MOFTL-ANN shows improvement of 17.42%, 6.81%, 10.77% and 59.69% MAPE values for England region and 4.20%, 4.16%, 1.14% and 21.85% MAPE values for ERCOT region over NSGA-II-ANN, FTL-ANN, BPNN, and GRNN.

Enabling Efficient and Privacy-Preserving Aggregation Communication and Function Query for Fog Computing-Based Smart Grid

Compared with the traditional grid, smart grid involves a lot of advanced technologies and applications. However, due to rapid development, it faces a challenge to balance privacy, security, efficiency, and functionality. In this paper, we build a fog computing-based smart grid model and then present an efficient and privacy-preserving scheme which supports aggregation communication and function query based on the proposed model. To the best of our knowledge, this is the first concrete solution focusing on aspects of aggregation communication and data availability (e.g., function query) simultaneously in smart grid. In doing so, we utilize geographically distributed massive fog nodes and a centralized cloud server to achieve low-latency communication and electricity data storage. Encrypted under a double trapdoor cryptosystem, the usage data can be efficiently aggregated by fog nodes. Which supports the service provider to dynamically control and distribute the electricity. With outsourcing the encrypted usage data to the cloud, our scheme allows the service provider to launch various function queries on encrypted usage data, which is necessary for its services (e.g., billing), while letting users to have control of their own data. Therefore, our scheme can be applied to more complex smart grid applications compared with other solutions. Security proofs and analyses show that our scheme guarantees data privacy, confidentiality, authentication, and integrity. Finally, we compare our scheme with related works in terms of functionalities and most importantly, implement the scheme in a simulation environment. Experimental results indicate that our scheme is efficient with low computation and communication overheads.

Residential battery sizing model using net meter energy data clustering

The high upfront costs of batteries have limited the investment in retrofit residential energy storage systems for solar customers. Battery size is one of the most important factors that impact the financial return since it determines the major operational capabilities of the solar-coupled storage system. To select the optimal battery size for a photovoltaic solar customer, it is important to perform an analysis taking account of the customer’s on-site generation and consumption characteristics. However, in most cases there are insufficient pre-existing data of the required quality making it difficult to perform such analysis. In this paper, we propose a model that can achieve satisfactory battery sizing results with a limited amount of net meter electricity data. The model uses K-means clustering on customer net meter electricity data to discover important information to extrapolate limited input net/gross meter energy data and uses this in a techno-economic simulation model to determine the optimal battery size. The approach is validated using a set of 262 solar households, two tariff structures (flat and Time-of-Use) and a naive forecasting method as a comparison to the proposed model. The results indicate that the proposed model outperforms the alternative baseline model and can work with as little as one month of net meter energy data for both of the evaluated tariff structures. On average, the model results in 0.10 normalised root mean squared error in yearly battery savings and net present values, 0.07 normalised root mean squared error in annual electricity costs and a r-squared value of 0.717 in finding the optimal size of batteries. Moreover, this study reveals a linear correlation between the used clustering validity index (Davies-Bouldin Index), and errors in estimated annual battery savings which indicates that this index can be used as a metric for the developed battery sizing approach. With the ongoing rollouts of net meters, the proposed model can address the data shortage issue for both gross and net meter households and assist end users, installers and utilities with their battery sizing analysis.

Forecasting Carbon Dioxide Emission of Asian Countries Using ARIMA and Simple Exponential Smoothing Models

Industrial revolution has increased the level of pollution due to emission of carbon dioxide in the past fewdecades. Resulting changes in the world’s climate are seen in the form of an increase in temperature which causes heatwaves, diseases related to skin, cardiovascular and other respiratory illnesses in humans. According to a past study,overall contribution of continent Asia is 49% - 50% in the production of carbon dioxide. Therefore, data of carbondioxide emission, heat and electricity, manufacturing industries, residential and commercial buildings, transport andother sources of Asian countries namely: Japan, Bangladesh, China, Pakistan, India, Sri Lanka, Iran, Singapore, andNepal from 1971 to 2014 have been taken for the present study. Empirical analysis shows that heat and electricity arethe main cause to increase carbon dioxide emission in Pakistan, Bangladesh, India, Iran and Sri Lanka. Whereas thisemission is increased in China due to residential and commercial buildings and transport variables. On the other hand,for Nepal and Singapore, these variables do not play any significant role in CO2 emission. Simple exponentialsmoothing (SES) and ARIMA models are used to forecast CO2 emissions. Furthermore, simple exponential smoothingis found appropriate for Pakistan and Sri Lanka based on the minimum FMAE. Whereas, for Japan, China, India, Iranand Singapore, ARIMA model is found suitable as it has minimum FMAE value. In Nepal and Bangladesh, bothmodels have significant differences, so any one of them can be used for forecasting.

Forecasting Carbon Dioxide Emission of Asian Countries Using ARIMA and Simple Exponential Smoothing Models

Industrial revolution has increased the level of pollution due to emission of carbon dioxide in the past fewdecades. Resulting changes in the world’s climate are seen in the form of an increase in temperature which causes heatwaves, diseases related to skin, cardiovascular and other respiratory illnesses in humans. According to a past study,overall contribution of continent Asia is 49% - 50% in the production of carbon dioxide. Therefore, data of carbondioxide emission, heat and electricity, manufacturing industries, residential and commercial buildings, transport andother sources of Asian countries namely: Japan, Bangladesh, China, Pakistan, India, Sri Lanka, Iran, Singapore, andNepal from 1971 to 2014 have been taken for the present study. Empirical analysis shows that heat and electricity arethe main cause to increase carbon dioxide emission in Pakistan, Bangladesh, India, Iran and Sri Lanka. Whereas thisemission is increased in China due to residential and commercial buildings and transport variables. On the other hand,for Nepal and Singapore, these variables do not play any significant role in CO2 emission. Simple exponentialsmoothing (SES) and ARIMA models are used to forecast CO2 emissions. Furthermore, simple exponential smoothingis found appropriate for Pakistan and Sri Lanka based on the minimum FMAE. Whereas, for Japan, China, India, Iranand Singapore, ARIMA model is found suitable as it has minimum FMAE value. In Nepal and Bangladesh, bothmodels have significant differences, so any one of them can be used for forecasting.

Sensors, sense-making and sensitivities: UK household experiences with a feedback display on energy consumption and indoor environmental conditions

Smart metering of domestic energy use allows consumer feedback through in-home displays (IHDs), websites or smart phone apps. Research has illustrated the need for additional ‘sense-making’ information to help households make informed energy-related decisions. This study investigates how household members respond when energy consumption data is integrated with information on indoor environmental conditions (IECs) and coupled with advice on energy saving actions. An integrated system of energy meters and IEC sensors was trialled in 19 predominantly social housing properties in the Midlands (England). Households were provided with a tablet computer and feedback was provided via a dedicated ‘Energy Dashboard’ web-based software application (app). The app was designed in collaboration with the social housing provider to display electricity and gas consumption data as well as data on three IECs: relative humidity, carbon dioxide and temperature. This paper draws on the findings from two rounds of semi-structured interviews with participants. All respondents using the app reported that they made use of the IEC data within the sense-making process, finding temperature and humidity to be useful in linking energy consumption, activities and household conditions. Interpretation of IEC data tended to increase with time as understanding increased. However, different users ‘noticed’, ‘interpreted’ and ‘enacted’ information differently as they integrated this with other sources of information, such as feedback from household members and experiential knowledge. The findings suggest that, whilst incorporating greater contextual information, such as IECs, into feedback displays can help users make sense of domestic energy consumption, the outcomes of the sense-making process will be different for different households. Nevertheless, the provision of such information appears to support householders to make decisions about their energy management that they feel appropriate for their household’s wellbeing needs, within the bounds of their agency.

A Homomorphic-Based Multiple Data Aggregation Scheme for Smart Grid

Smart meters have been installed to report users' real-time electricity consumption data to the utility supplier periodically, which enables fine-grained energy supply, as the utility supplier can adjust its supplement based on users' consumptions. However, these real-time electricity data can also reveal the behaviors of the inhabitants; for example, the real-time electricity consumption data can reveal if the inhabitant is at home, if the television is working, and so on. People are reluctant to disclose these kinds of personal information. In this paper, we come up with a smart meter data aggregation scheme based on the Paillier homomorphic cryptosystem, this aggregation scheme enables a utility supplier to get the total consumption of all the smart meters, while the utility supplier is unable to get the consumption data of a single smart meter. In addition, the proposed scheme enables the smart meter to report multiple types of data in one reporting message, which makes it possible for the supplier to conduct the variance analysis and the one-way analysis of variance on the data. The formal security analysis shows that the proposed scheme is semantically secure. The experiment results show that the proposed scheme can reduce the computation cost both on the smart meter side and on the aggregator side.

Conditional Bias Robust Estimation of the Total of Curve Data by Sampling in a Finite Population: An Illustration on Electricity Load Curves

AbstractFor marketing or power grid management purposes, many studies based on the analysis of total electricity consumption curves of groups of customers are now carried out by electricity companies. Aggregated totals or mean load curves are estimated using individual curves measured at fine time grid and collected according to some sampling design. Due to the skewness of the distribution of electricity consumptions, these samples often contain outlying curves which may have an important impact on the usual estimation procedures. We introduce several robust estimators of the total consumption curve which are not sensitive to such outlying curves. These estimators are based on the conditional bias approach and robust functional methods. We also derive mean square error estimators of these robust estimators, and finally, we evaluate and compare the performance of the suggested estimators on Irish electricity data.

Characterisation of Australian apartment electricity demand and its implications for low-carbon cities

Understanding of residential electricity demand has application in efficient building design, network planning and broader policy and regulation, as well as in planning the deployment of energy efficiency technologies and distributed energy resources with potential emissions reduction benefits and societal and household cost savings. Very few studies have explored the specific demand characteristics of apartments, which house a growing proportion of the global urban population.We present a study of apartment electricity loads, using a dataset containing a year of half-hourly electricity data for 6,000 Australian households, to examine the relationship between dwelling type, demographic characteristics and load profile. The focus on apartments, combined with the size of the data set, and the representative seasonal load profiles obtained through clustering full annual profiles, is unique in the literature. We find that median per-occupant household electricity use is 21% lower for apartments than for houses and that, on average, apartments have lower load factor and higher daily load variability, and show greater diversity in their daily peak times, resulting in a lower coincidence factor for aggregations of apartment loads. Using cluster analysis and classification, we also show the impact of dwelling type on the shape of household electricity load profiles.

Smarter Cities: Cleaning Electricity, Gas and Water Metered Consumption Data for Social and Urban Research

Smarter Cities: Cleaning Electricity, Gas and Water Metered Consumption Data for Social and Urban Research

Comparative study of data driven methods in building electricity use prediction

The energy use prediction of building systems is crucial to design a high-efficiency building and maintain low energy consumption operation, which is also important in optimizing building system control and retrofitting. This paper demonstrates a comparative study of four data-driven methods used in online building energy predictions involving large-scale data extracted from several types of buildings. The characteristics of building electricity use and data reliability were addressed through the data pre-treatment process including visualization, cleaning, parsing and filtering. Mathematical algorithms and their applications in previous studies were summarized and compared, and evaluation methods were developed. The performance and suitable application scenarios of the proposed algorithms were conducted via the comparison of monitoring data and predicted results. The study indicates that the most complex method which requires the highest computation ability, i.e., the Artificial Neural Network (ANN), does not lead to the highest accuracy, while as the fastest computation method, Gaussian Process Regression (GPR) usually has the results with the lowest accuracy. Support Vector Machine (SVM) and Multivariate Linear Regression (MLR) methods usually perform better in the case scenarios studied. All the prediction accuracies can meet the requirements of RMSE <30% and NMBE <10% proposed by ASHRAE, and the computation time varies from less than 1 s to 22 s per prediction. All these methods/algorithms worked well for buildings with stable energy use patterns. For buildings with complex and unstable occupancy schedules and energy use patterns, MLR and SVM methods have the ability to achieve a high accuracy with fast computation speed.