Daily Electricity Usage Research Articles

IoT based Smart Home Energy Tracker and Saver

The knowledge about the amount of energy used every month or day at home is the initial step to reduce the energy usage. So, the proposed research focusses on developing a smart energy management system using the IoT to track the daily electricity usage and enhance the energy efficiency at home. The system employs network of sensors placed throughout the home to monitor energy usage in real-time. The information gathered is transmitted to the Blynk Cloud and visualized through the Blynk app. The chart displayed in the Blynk app enables the user to identify the excessive energy usage and reduce both consumption and the electricity bills. By integrating IoT technology and user-friendly control interfaces, this research aims to provide homeowners with actionable insights to minimize energy waste, reduce utility bills, and contribute to environmental conservation.

Sistem Monitoring Berbasis IoT pada Pembangkit Listrik Tenaga Mikrohidro Tangsijaya

The Micro Hydro Power Plant of Gunung Halu was established in 2007. While electricity from the national power grid is available in the village of Gunung Halu, not all hamlets receive a consistent power supply, particularly those with hilly topography and areas surrounded by forests like Dusun Tangsijaya. The existence of the micro hydro power plant in Tangsijaya is supported by a stable water flow from the river. However, one of the challenges faced by the operator is the lack of a monitoring system for the micro hydro power generator that can provide information about its condition, such as power output and electric current. Until now, the operator has checked the data by visiting the location of the micro hydro power plant directly, which is located outside the residential area. Manual recording relies on the existence of human resources and produces results with a low level of accuracy. In this community service activity, a monitoring system was installed at the Tangsijaya's micro hydro power plant. Leveraging Internet of Things (IoT) technology, this system can display data in real time, so that data visualization can be monitored by relevant parties. With a monitoring system, micro-hydro operators can control the usage and distribution of the generated electricity. In addition to obtaining records of daily electricity usage data, recorded data provide very valuable information in assessing current and potential electricity usage for making decisions related to electricity production. After the implementation of the monitoring system, the main benefits are felt by microhydro operators where the work processes they do become more efficient. Efficiency is obtained in terms of travel distance where operators do not need to visit the PLTMH location directly, and also efficiency in the recording process which no longer needs to be done manually.

Characterizing residential sector load curves from smart meter datasets

Understanding how and when residential electricity is used throughout the day is integral to the successful implementation of potential residential demand management strategies. Our analysis characterizes the daily hourly load profiles of approximately 160,000 residential electricity customers across the Southern California Edison (SCE) service area during the period spanning 2015 to 2016 and 2018 to 2019 across weekends, weekdays, seasons, and climate zones. We find that total daily electricity usage was highest in the hottest months of the year compared to milder months, particularly for households located in the hottest climate zones. The most energy-consumptive hours occurred during the mid-afternoon during the hottest months, in contrast to early evening high consumption in cooler months. We find that customers with average daily consumption at or above the 80th percentile cumulatively consume over 40% of electricity during the hottest months of the year residential load, while the bottom half of customers cumulatively consume <25% of the total residential load. The disparities in electricity usage across SCE households are higher in the mid-day, especially in milder months across all regions, and in mild climate zones compared to hotter climate zones since loads are not as dependent on high HVAC loads.

Applicability of energy consumption prediction models in a department store: A case study

Obtaining an accurate picture of energy consumption in public spaces is a critical first step for predicting and enhancing energy efficiency. In practice, such calculations are often complicated, necessitating use of artificial intelligence models which can account for multiple factors. In this study, we obtained four years of daily electricity usage, people flow, and ambient factor data from a department store in Taiwan to build an artificial intelligence model for predicting energy consumption. Previous literature on public spaces have mainly used Long Short-Term Memory (LSTM), gated recurrent unit (GRU), and other recurrent neural network (RNN) mechanisms. However, as our dataset spanned four years, we hypothesized that seasonal and trend decomposition using Loess (STL) was applicable as our data likely exhibited long cyclic patterns. We built and compared prediction results of three neural networks: LSTM-classic, STL, and GRU. As expected, the STL model provided better overall results (indicating a strong seasonality component in our data), as well as an optimal balance between accuracy and processing time. This case study provides a framework for others seeking to build neural networks for predicting and managing energy consumption in public spaces, and forms a theoretical basis for further discussion of energy efficiency topics.

Energy management of smart homes over fog-based IoT architecture

Existing research studies on home automation systems mostly conserve energy by modeling the occupancy of users within home. Some others apply statistical approaches on the survey data about usage of appliances. Consequently, these research works either reduce wastage of electricity through automation or achieve energy efficiency based on appliances’ usage estimations. However, they do not provide energy consumption modeling which is human comfort centric and also validated through practical implementation in real-world smart homes. We present a Markov-chain-based probabilistic model to obtain users stochastic activity patterns which are used to forecast the energy consumption in a smart home environment. These predictions are then leveraged by our novel comfort aware energy saving mechanism named as prediction- and feedback-based proactive energy conservation (PF-PEC) algorithm. The PF-PEC algorithm reduces the total energy consumption while ensuring standard human comfort. Furthermore, a fog-based Internet of Things (IoT) architecture is implemented and deployed in a smart home to efficiently incorporate the proposed algorithm in real-world scenarios. Experimental results show up to 36% energy conservation, marking substantial reduction in daily electricity usage.

Thermal and energy performance evaluation of a full-scale test cabin equipped with PCM embedded radiant chilled ceiling

The escalating global demand for space cooling has led to the emergence of new cooling technologies, including the phase change material embedded radiant chilled ceiling (PCM-RCC) system. This technology improves energy efficiency and indoor environmental quality, while also offering demand-side flexibility. The present study experimentally evaluates the thermal efficiency and energy performance of a PCM-RCC system in a full-scale test cabin equipped with PCM panels. Here, the transient thermal behaviour of PCM ceiling panels besides the cooling energy delivered during charging-discharging cycles are examined. The indoor thermal comfort and peak electricity demand reduction enabled by the present PCM-RCC are also discussed. The results reveal that chilled water circulation for 4–5 h overnight was sufficient to fully recharge the PCM panels. Over 80% of the occupancy time was classified as “Class B″ thermal comfort according to ISO 7730. The system's daily electricity usage was mostly concentrated during off-peak hours, accounting for ∼70% of the total usage. While the controlling schedule used in this study responded to the transient thermal behaviour of the indoor space and PCM ceiling panels, a more dynamic, predictive schedule is necessary to improve the system's overall efficiency and further enhance indoor thermal comfort in response to the changing environmental conditions.

Pre-SEMMS: A Design of Prepaid Smart Energy Meter Monitoring System for Household Uses Based on Internet of Things

The use of prepaid smart electricity services from the State Electricity Company (SEC) for the general public in Indonesia has increased. It is because prepaid electricity has several advantages compared to postpaid electricity. One of them is that it is becoming easier for customers to manage and control their daily electricity usage. Customers can also estimate their total electricity consumption each month. However, customers still have to manually view the information on the electricity meter to find data and information on prepaid electricity. It can make it difficult for customers if the electricity meter is placed outside the house, which is quite far away. Customers must also constantly monitor the use of electricity. This article discusses a prepaid smart electricity consumption monitoring design using Internet of Things technology. The design is carried out without changing the standard prepaid electricity meter system from the SEC. However, a KWH meter tool has been developed that has been combined with Internet of Things technology and can calculate and monitor electricity usage. The results show that with this system, customers can more easily find out and monitor their daily electricity consumption.&#x0D;

Prediction of Building Electricity Consumption Based on Joinpoint−Multiple Linear Regression

Reliable energy consumption forecasting is essential for building energy efficiency improvement. Regression models are simple and effective for data analysis, but their practical applications are limited by the low prediction accuracy under ever-changing building operation conditions. To address this challenge, a Joinpoint–Multiple Linear Regression (JP–MLR) model is proposed in this study, based on the investigation of the daily electricity usage data of 8 apartment complexes located within a university in Xiamen, China. The univariate model is first built using the Joinpoint Regression (JPR) method, and then the remaining residuals are evaluated using the Multiple Linear Regression (MLR) method. The model contains six explanatory variables, three of which are continuous (mean outdoor air temperature, mean relative humidity, and temperature amplitude) and three of which are categorical (gender, holiday index, and sunny day index). The performance of the JP–MLR model is compared to that of the other four data-driven algorithm models: JPR, MLR, Back Propagation (BP) neural network, and Random Forest (RF). The JP–MLR model, which has an R2 value of 95.77%, has superior prediction performance when compared to the traditional regression-based JPR model and MLR model. It also performs better than the machine learning-based BP model and is identical to that of the RF model. This demonstrates that the JP–MLR model has satisfactory prediction performance and offers building operators an effective prediction tool. The proposed research method also provides also serves as a reference for electricity consumption analysis in other types of buildings.

Edge Computing Based Electricity-Theft Detection of Low-Voltage Users

Electricity theft of low voltage (LV) users could result not only in the escalation of power loss but also in dangerous electric shock. Since LV users are served by distribution transformers, electricity theft of an LV user will cause line loss escalation of the associated distribution serving zone (DTSZ). Therefore, it seems promising to identify anomaly users of electricity theft with a Granger causality test to find out the user causing an escalation of line loss in DTSZ with time series of users’ usage and line loss. However, meters of LV users in severe environments occasionally suffer from communication failure to upload metering data to the head end of advanced metering infrastructure (AMI), which could distort the daily electricity usage of the associate user. Consequently, it could cause false alarms unavoidably once we detect electricity theft with these distorted data. Since the distribution transformer unit (DTU) collects metering data of LV users within associate DTSZ without distortion, an edge computing–based electricity theft detection approach is proposed in this article. The correlation between line loss of a DTSZ and electricity usage of anomaly users of electricity theft is first analyzed. Thereafter, the Granger causality test is used to identify anomaly users with authentic usage data with edge computing in DTU. Finally, the abnormal data and the data repaired by different missing data filling algorithms are used on the main station to detect electricity theft. Numerical simulation suggests that although missing data completion could recover information in missing data partially, it could result in notable false positive alarms in electricity theft, while the proposed method based on edge computing can completely eliminate the data distortion caused by communication failure.

Predicting city-scale daily electricity consumption using data-driven models

• We studied how city electricity use is influenced by weather and COVID-19 pandemic. • Seven data-driven models were applied and evaluated for data of three cities. • Gradient boosting tree model delivers the most accurate prediction with CVRMSE of 4%−6%. • 1 °C increase of ambient temperature drives up the three cities electricity usage by around 5%. • COVID-19 curtailment reduced city-scale electricity usage by 2%−12%. Accurate electricity demand forecasts that account for impacts of extreme weather events are needed to inform electric grid operation and utility resource planning, as well as to enhance energy security and grid resilience. Three common data-driven models are used to predict city-scale daily electricity usage: linear regression models, machine learning models for time series data, and machine learning models for tabular data. In this study, we developed and compared seven data-driven models: (1) five-parameter change-point model, (2) Heating/Cooling Degree Hour model, (3) time series decomposed model implemented by Facebook Prophet, (4) Gradient Boosting Machine implemented by Microsoft lightGBM, and (5) three widely-used machine learning models (Random Forest, Support Vector Machine, Neural Network). Seven models are applied to the city-scale electricity usage data for three metropolitan areas in the United States: Sacramento, Los Angeles, and New York. Results show seven models can predict the metropolitan area's daily electricity use, with a coefficient of variation of the root mean square error (CVRMSE) less than 10%. The lightGBM provides the most accurate results, with CVRMSE on the test dataset of 6.5% for Los Angeles, 4.6% for Sacramento, and 4.1% for the New York metropolitan area. These models are further applied to explore how extreme weather events (e.g., heat waves) and unexpected public health events (e.g., COVID-19 pandemic) influence each city's electricity demand. Results show weather-sensitive component accounts for 30%–50% of the total daily electricity usage. Every degree Celsius ambient temperature increase in summer leads to about 5% (4.7% in Los Angeles, 6.2% in Sacramento, and 5.1% in New York) more daily electricity usage compared with the base load in the three metropolitan areas. The COVID-19 pandemic reduced city-scale electricity demand: compared with the pre-pandemic same months in 2019, daily electricity usage during the 2020 pandemic decreased by 10% in April and started to rebound in summer.

A new fuzzy logic based approach for optimal household appliance scheduling based on electricity price and load consumption prediction

ABSTRACT Smart Home Energy Management Systems (HEMS) constitute a vital necessity for optimizing electricity usage and saving energy in smart grids. However, these systems rely on dynamic factors that are stochastic and difficult to predict, such as the load consumption and electricity prices. Therefore, constructing an efficient control system for residential buildings requires an accurate prediction process of the associated parameters. This paper proposes an integrated predictive control system that consists of both predictive model and Demand Response (DR) scheme to predict and control the daily electricity usage in the residential sector. First, a Long Short-Term Memory-based (LSTM) optimized predictive model is implemented for predicting both the hourly load consumption and electricity price for a typical smart home. Then, the predicted data are transmitted to a DR fuzzy logic-based controller that can optimally schedule the home appliances usage. In comparison with the state-of-the-art prediction techniques for the residential load consumption and electricity price, the proposed LSTM predictive model outperforms Linear Regression (LR), Decision Tree (DT), Support Vector Regression (SVR), and Ensembled Boosted Trees (EBT). Moreover, the proposed DR-FIS controller has shown good results in terms of reducing the electricity cost by selecting the optimal time schedule.

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.

A data-driven strategy to forecast next-day electricity usage and peak electricity demand of a building portfolio using cluster analysis, Cubist regression models and Particle Swarm Optimization

This study presents a new strategy using cluster analysis, Cubist regression models and Particle Swarm Optimization to forecast next-day total electricity usage and peak electricity demand of a building portfolio. Cluster analysis with a combined dissimilarity measure was first used to group daily electricity usage profiles of the building portfolio. The clustering result was then considered in the training of the Cubist-based forecasting models in order to improve the forecasting accuracy. A Particle Swarm Optimization algorithm was used to determine the optimal parameters in the cluster analysis to further improve the forecasting accuracy. The performance of this strategy was evaluated using the electricity usage data of 40 university buildings. The results showed that the difference between the measured and predicted daily total electricity usage was 4.7% in terms of the coefficient of variation of the root-mean-squared error (CV(RMSE)) and 3.3% in terms of mean absolute percentage error (MAPE) and the difference between the measured and predicted daily peak load was 6.0% in CV(RMSE) and 5.3% in MAPE. The proposed strategy can effectively improve the accuracy of the forecasting result by up to 18.1% and 12.2% when compared to the strategy which did not consider the clustering result of the daily electricity usage profiles in the forecasting models and the strategy which considered the clustering result obtained using a single dissimilarity measure. Compared to the mean level of the nine strategies that used different regression methods, the proposed strategy can improve the forecasting accuracy by up to 42.2%. The results of this study can be further used to assist in the development of building optimal control and operation strategies.

A Simplified Method: System Size and Cost Estimation of Grid Connected Photovoltaic (GCPV) System

There are various types of Renewable Energy (RE) that has been studied by many researchers in order to find a cost-effective energy generation in reducing the non-renewable energy source. In this study, the solar energy was suggested in order to apply the Net Energy Metering (NEM) scheme that has been introduced effective on 1st January 2019, by Malaysian government in achieving 20% national RE target in electricity power mix, reducing the dependency on imported fossil fuels. Based on the data and results, the suitable size of solar panel for a house can be determined based on the daily electricity usage. The findings show that the system size and the cost itself also depends on the estimated amount solar generated set by the consumer.

Implementation of Artificial Neural Network Method for Estimating Connected Power and Electric Energy Consumption

Abstract—Electricity is vital for modern society’s welfare. Daily electricity usage depended on the customers’ type. Hence, there was a difference between the connected power with consumption. Therefore, there needed an estimation method for long-term connected power and energy consumption to improve the safety of energy management and operation plan for the generator. This research used the Artificial Neural Network method with a backpropagation algorithm model to estimate the connected power and electricity consumption. This method has the advantage of following past patterns after the training process. This research used data such as total population, Gross Regional Domestic Product, total customers, produced energy, remaining energy, distribution loss, total transformer, peak load, and load factor as the independent data. The energy consumption and connected power served as the dependent data. The data was taken from Srengat Network Service Unit, East Java, for ten years, which started in 2008. This research used literature study, information and data collection, information and data process, data estimation and analysis, and conclusion as the procedures. Based on the results, the best network structure was 9-9-2 with the 10-6 goal, 0.9 momentum value, and 0.15 learning rate to produce the smallest Mean Squared Error of 0.00442 in 2015, Mean Absolute Percentage Error of 7.88% for the connected power, and 11.27% on electricity consumption target.

An agglomerative hierarchical clustering-based strategy using Shared Nearest Neighbours and multiple dissimilarity measures to identify typical daily electricity usage profiles of university library buildings

Abstract This study presents an agglomerative hierarchical clustering-based strategy using Shared Nearest Neighbours and multiple dissimilarity measures to identify typical daily electricity usage profiles of university library buildings. The proposed strategy takes the advantages of three dissimilarity measures (i.e. Euclidean distance, Pearson distance and Chebyshev distance) to calculate the difference between daily electricity usage profiles. Two-year hourly electricity usage data collected from two different university library buildings were employed to evaluate the performance of this strategy. It was shown that this strategy, which considered both magnitude dissimilarity and variation dissimilarity simultaneously, can identify more informative typical daily electricity usage profiles, in comparison with other twelve clustering-based strategies which used a single dissimilarity measure. Some interesting information related to building energy usage behaviours was also discovered with the help of visualisation techniques. Additional or hidden information discovered using this strategy can potentially be useful for fault detection and diagnosis and performance enhancement of library buildings.

Energy Theft Detection Using Gradient Boosting Theft Detector With Feature Engineering-Based Preprocessing

For the smart grid energy theft identification, this letter introduces a gradient boosting theft detector (GBTD) based on the three latest gradient boosting classifiers (GBCs): 1) extreme gradient boosting; 2) categorical boosting; and 3) light gradient boosting method. While most of existing machine learning (ML) algorithms just focus on fine tuning the hyperparameters of the classifiers, our ML algorithm, GBTD, focuses on the feature engineering-based preprocessing to improve detection performance as well as time-complexity. GBTD improves both detection rate and false positive rate (FPR) of those GBCs by generating stochastic features like standard deviation, mean, minimum, and maximum value of daily electricity usage. GBTD also reduces the classifier complexity with weighted feature-importance-based extraction techniques. Emphasis has been laid upon the practical application of the proposed ML for theft detection by minimizing FPR and reducing data storage space and improving time-complexity of the GBTD classifiers. Additionally, this letter proposes an updated version of the existing six theft cases to mimic real-world theft patterns and applies them to the dataset for numerical evaluation of the proposed algorithm.

Identification of typical building daily electricity usage profiles using Gaussian mixture model-based clustering and hierarchical clustering

This paper presents a clustering-based strategy to identify typical daily electricity usage (TDEU) profiles of multiple buildings. Different from the majority of existing clustering strategies, the proposed strategy consists of two levels of clustering, i.e. intra-building clustering and inter-building clustering. The intra-building clustering used a Gaussian mixture model-based clustering to identify the TDEU profiles of each individual building. The inter-building clustering used an agglomerative hierarchical clustering to identify the TDEU profiles of multiple buildings based on the TDEU profiles identified for each individual building through intra-building clustering. The performance of this strategy was evaluated using two-year hourly electricity consumption data collected from 40 university buildings. The results showed that this strategy can discover useful information related to building electricity usage, including typical patterns of daily electricity usage (DEU) and periodical variation of DEU. It was also shown that this proposed strategy can identify additional electricity usage patterns with a less computational cost, in comparison to two single-step clustering strategies including a Partitioning Around Medoids-based clustering strategy and a hierarchical clustering strategy. The results obtained from this study could be potentially used to assist in improving energy performance of university buildings and other types of buildings.

Short analysis for the growth of solar electricity usage

Solar electricity generation has been investigated in three different sectors as residential, commercial and industrial. This study aims to forecast the solar electricity generation in 5 years by using statistical analysis and make projection of predictions about what could the solar technology can become in Turkey, using USA data on solar electricity generation since statistical analysis is made by using evaluation of regression analysis for these sectors. The results show that the PV generation for the sectors by years such as distributed solar photovoltaic generation value varies from 10.41 MkW h (1989) to 10,464.61 MkW h (2016) for the residential sector; from 14.72 MkW h (1989) to 7179.62 MkW h (2016) for the commercial sector; and from 3.26 MkW h (1989) to 1822.60 MkW h (2016) for the industrial sector. Additionally, forecast of energy distribution (million kW h) has been carried out that from 12783.96 to 28738.31; from 6524.67 to 8018.74; from 1927.91 to 3435.58 with respect to sectors of residential, commercial and industrial, respectively. These results are obtained with the R2 values (0.968667; 0.932558; 0.716024). Consequently, it would not be wrong to express that the solar energy is no more a novelty, but a necessity with the daily electricity usage. In this study, the novelty lies in study’s unique combination of analysing goals with a benchmarking evaluation between USA and Turkey. This is illustrated with the forecasting energy distribution of over 8%, a component usage of at least three equivalent sectors.

Optimal Operation Control for Multiple BESSs of a Large-Scale Customer Under Time-Based Pricing

This paper presents an online optimal operation framework for multiple battery energy storage systems (BESSs) of a large-scale customer under time-based pricing. Many publications have been reported on optimal battery operation techniques but most of them were analyzed in a simulation environment or a specifically designed test bed. However, this paper focuses on implementing the proposed scheme into actual multiple battery storage units and investigating the performance during long-term field operation. The operation framework consists of two levels: optimal scheduling and real-time dispatch. The optimal scheduling is calculated every hour, using a model predictive control based nonlinear optimization model, to minimize the daily electricity usage cost while regulating the peak. The real-time dispatch determines final commands to multiple battery systems by monitoring the system state and checking for any violations of the operation constraints. The two-level control scheme was designed to handle uncertainty in forecast load and estimated state-of-charge levels of batteries. The operation method was applied into the energy management system supervising one lithium-polymer BESS and two lead-acid BESSs of an industrial site. Comprehensive field operation results prove the reliability and effectiveness of the optimal operation framework.