Monthly Electricity Consumption Research Articles

A novel time-varying grey Fourier model for variable amplitude seasonal fluctuation sequences

PurposeA time-varying grey Fourier model (TVGFM(1,1,N)) is proposed for the simulation of variable amplitude seasonal fluctuation time series, as the performance of traditional grey models can't catch the time-varying trend well.Design/methodology/approachThe proposed model couples Fourier series and linear time-varying terms as the grey action, to describe the characteristics of variable amplitude and seasonality. The truncated Fourier order N is preselected from the alternative order set by Nyquist-Shannon sampling theorem and the principle of simplicity, then the optimal Fourier order is determined by hold-out method to improve the robustness of the proposed model. Initial value correction and the multiple transformation are also studied to improve the precision.FindingsThe new model has a broader applicability range as a result of the new grey action, attaining higher fitting and forecasting accuracy. The numerical experiment of a generated monthly time series indicates the proposed model can accurately fit the variable amplitude seasonal sequence, in which the mean absolute percentage error (MAPE) is only 0.01%, and the complex simulations based on Monte-Carlo method testify the validity of the proposed model. The results of monthly electricity consumption in China's primary industry, demonstrate the proposed model catches the time-varying trend and has good performances, where MAPEF and MAPET are below 5%. Moreover, the proposed TVGFM(1,1,N) model is superior to the benchmark models, grey polynomial model (GMP(1,1,N)), grey Fourier model (GFM(1,1,N)), seasonal grey model (SGM(1,1)), seasonal ARIMA model seasonal autoregressive integrated moving average model (SARIMA) and support vector regression (SVR).Originality/valueThe parameter estimates and forecasting of the new proposed TVGFM are studied, and the good fitting and forecasting accuracy of time-varying amplitude seasonal fluctuation series are testified by numerical simulations and a case study.

AN AGENT-BASED COMPUTATIONAL MODEL ON HOUSEHOLD ELECTRICITY CONSUMPTION IN INDIAN CITIES

ABSTRACT Traditional methods of planning and managing cities often adopt a simplified linear approach to understanding their composition and functioning. These approaches have led to the creation of many undesirable and non-functional cities. However, over the last few decades, there has been a radical shift in the approach to investigating cities through the adoption of complexity science and its various concepts. Unfortunately, this paradigm shift has been limited to only a handful of developed Western nations. This study proposes an extendable agent-based computational model for assessing household electricity consumption patterns in cities of developing nations. The model is demonstrated using an urban precinct in India as a representative case. It simulates the monthly electricity consumption of an urban precinct in Nagpur by combining the significant factors impacting household electricity consumption in developing nations, with the household's electricity consumption process in relation to outdoor weather conditions, and the heterogeneity in occupants’ behavior. The predictive validation of the model using the “BehaviorSpace” tool yielded promising results, with the model consistently and accurately predicting the growth of variables and household electricity consumption patterns. The model also successfully generated multiple future scenarios, showcasing its capacity to forecast the future implications of policy interventions. The simulation results indicated that household electricity consumption in Indian cities is driven by population growth and income levels. Additionally, it suggests that widely practiced rooftop solar panel installation policies may not be a viable approach to achieving sustainability in this sector. This study finds that the “emergent urbanism” approach has the potential to revitalize the urban energy planning process through an effective framework for comprehensively understanding the phenomena and addressing its various complexities.

A Study of Households’ Perception towards Solar Panel Installation Case Study: Villagers of Techno-park nearby Faculty of Technology, University of Sri Jayewardenepura

In Sri Lanka, energy demand has been exponentially increasing with the outage of non-renewable energy sources. There is significant solar intensity in most of the areas in Sri Lanka. This study focused on the domestic solar power system since domestic solar panel usage (700 MW) is the country's highest contributor to solar energy usage. A photovoltaic (PV) system can supply electric energy to loads by directly converting solar energy through the photovoltaic effect with a flexible structure. This study aimed to analyze households' perceptions of solar panel installation. The study was conducted within a 5 km radius around the Faculty of Technology, the University of Sri Jayewardenepura, to analyze the awareness of solar-related technologies. A systematically developed questionnaire was used for the survey to collect the peoples' awareness based on environmental, social & economic beliefs. Thirty residential places (30) were interviewed. According to the results, a residential house's average monthly electricity consumption is 137.67 kWh. The mean monthly income in the area is sixty-eight thousand one hundred and sixty-six rupees and sixty-seven cents. Moreover, most people had solar energy knowledge (93.33%). Among them, 53.57% of the majority were willing to install Domestic Solar PV systems for residential places. From the consumers, 76.66% of the majority had an average level of awareness of the Environmental benefits of using renewable energy and 70.00% of the majority had an excellent awareness of government intervention in rooftop solar energy projects such as "Soorya Bala Sangramaya‖. From the defined awareness levels, further analysis was done on the lowest level of Awareness of Solar Energy to identify their characteristics. The majority of them were those who had lower energy consumption, were over the age of 60 and had a monthly income below twenty thousand rupees. According to the consumers, the best strategy for promoting Solar PV Systems was to lower the prices of existing Solar PV systems. Regulating the efficiency of the Solar PV system was identified by the existing Solar PV system owners. 
 Keywords: Solar PV system, Techno-park, Photovoltaic panels

Urban water and electricity demand data for understanding climate change impacts on the water-energy nexus

As the climate crisis intensifies, it is becoming increasingly important to conduct research aimed at fully understanding the climate change impacts on various infrastructure systems. In particular, the water-electricity demand nexus is a growing area of focus. However, research on the water-electricity demand nexus requires the use of demand data, which can be difficult to obtain, especially across large spatial extents. Here, we present a dataset containing over a decade (2007–2018) of monthly water and electricity consumption data for 46 major US cities (2018 population >250,000). Additionally, we include pre-processed climate data from the North American Regional Reanalysis (NARR) to supplement studies on the relationship between the water-electricity demand nexus and the local climate. This data can be used for a number of studies that require water and/or electricity demand data across long time frames and large spatial extents. The data can also be used to evaluate the possible impacts of climate change on the water-electricity demand nexus by leveraging the relationship between the observed values.

Comparison Between ARIMA and EEMD+ARIMA Models in Forecasting Electricity Consumption

Accurate forecasting of future electricity consumption is necessary to create a satisfactory design for an electricity distribution system. To enhance forecasting accuracy, autoregressive integrated moving average (ARIMA) was compared with hybrid of ensemble empirical mode decomposition (EEMD) plus autoregressive integrated moving average (ARIMA) denoted by (EEMD+ARIMA), to know which model is better performing a historical US monthly electricity consumption from DEC-2000 to SEP-2022 were used. The data were divided into training set (90%) and testing set (10%) to insure the model accuracy. The mean absolute square error, root mean square error, mean absolute error and mean absolute percentage error measurements were used to test the ARIMA and hybrid EEMD+ARIMA performance, the results show that the hybrid EEMD+ARIMA outperforms ARIMA model with the lowest RMSE, MAE, MPE, MAPE, MASE. For the best model, Akaike Information Criterion and Bayesian Information Criterion were applied to choose the best. The results show that the AIC and BIC of the EEMD+ARIMA were lower than the ARIMA model, which indicates that the EEMD+ARIMA is better than the single ARIMA in forecasting of electricity consumption. The conclusion reveals that the hybrid EEMD+ARIMA provides more accurate forecasting and performs significantly better than the ARIMA in forecasting of electricity.

Research on energy-saving lighting control of high-rise building by the PID control algorithm

The lighting of high-rise buildings consumes a significant amount of electricity, making it essential to implement energy-saving measures. In this paper, the lighting of high-rise buildings was briefly analyzed, followed by a description of the proportion, integration, and differentiation (PID) control algorithm. To improve the efficiency of lighting control for energy conservation, the fuzzy PID control algorithm was analyzed. The self-tuning of parameters was achieved by utilizing the whale optimization algorithm (WOA) to develop a WOA-fuzzy PID control algorithm. Finally, experimental analysis was carried out. The simulation findings showed that the WOA-fuzzy PID algorithm had the shortest stabilization time (6.77 s), the smallest maximum overshoot (3.12%), and better anti-interference capability compared to the PID and fuzzy PID algorithms. Finally, it was found from practical application that the use of the WOA-optimized algorithm resulted in a 43.7% reduction in monthly electricity consumption. The findings suggest the effectiveness of the WOA-fuzzy PID algorithm in energy-efficient lighting control and its applicability to real-world high-rise buildings.

TCN Multi-time-scale Transformation and Temporal-Attention Neural Network for Monthly Electricity Consumption Forecasting

Background:: For the efficient and secure running of the power industry, accurate monthly electricity projections are crucial. Due to coupling variations and a variety of data resolutions, current approaches are still unable to accurately extract multidimensional time-series data. Objective:: For monthly electricity consumption forecasting, a multi-time-scale transformation and temporal attention neural network for a temporal convolutional network is proposed. Method:: First, a multi-time-scale compression model of temporal convolutional network is proposed, which compresses data on several time scales from different resolutions, such as the economy, weather, and historical load. Second, a multi-source temporal attention module is built to further dynamically extract crucial information. Finally, the decoding-encoding and residual connections' structure contributes to the prediction's improved resilience. Results:: The proposed method was compared with the state-of-the-art monthly load forecasting based on two years of historical data in a certain region, demonstrating its effectiveness. Conclusion:: Through the verification of local historical data, the proposed model was contrasted with cutting-edge monthly load forecasting techniques. The obtained results demonstrate the effectiveness.

Mapping urban living standards and economic activity in developing countries with energy data.

Urban data deficits in developing countries impede evidence-based planning and policy. Could energy data be used to overcome this challenge by serving as a local proxy for living standards or economic activity in large urban areas? To answer this question, we examine the potential of georeferenced residential electricity meter data and night-time lights (NTL) data in the megacity of Karachi, Pakistan. First, we use nationally representative survey data to establish a strong association between electricity consumption and household living standards. Second, we compare gridded radiance values from NTL data with a unique dataset containing georeferenced median monthly electricity consumption values for over 2 million individual households in the city. Finally, we develop a model to explain intra-urban variation in radiance values using proxy measures of economic activity from Open Street Map. Overall, we find that NTL data are a poor proxy for living standards but do capture spatial variation in population density and economic activity. By contrast, electricity data are an excellent proxy for living standards and could be used more widely to inform policy and support poverty research in cities in low- and middle-income countries.

Lattice-Based Homomorphic Encryption For Privacy-Preserving Smart Meter Data Analytics

Abstract Privacy-preserving smart meter data collection and analysis are critical for optimizing smart grid environments without compromising privacy. Using homomorphic encryption techniques, smart meters can encrypt collected data to ensure confidentiality, and other untrusted nodes can further compute over the encrypted data without having to recover the underlying plaintext. As an illustrative example, this approach can be useful to compute the monthly electricity consumption without violating consumer privacy by collecting fine-granular data through small increments of time. Toward that end, we propose an architecture for privacy-preserving smart meter data collection, aggregation and analysis based on lattice-based homomorphic encryption. Furthermore, we compare the proposed method with the Paillier and Boneh–Goh–Nissim (BGN) cryptosystems, which are popular alternatives for homomorphic encryption in smart grids. We consider different services with different requirements in terms of multiplicative depth, e.g. billing, variance and nonlinear support vector machine classification. Accordingly, we measure and show the practical overhead of using the proposed homomorphic encryption method in terms of communication traffic (ciphertext size) and latency. Our results show that lattice-based homomorphic encryption is more efficient than Paillier and BGN for both multiplication and addition operations while offering more flexibility in terms of the computation that can be evaluated homomorphically.

Towards Efficient Energy Consumption: Audit of Households Electricity Consumption in Ghana

Electricity from national grids remain the most reliable and affordable source of power to many households globally. However, in Ghana, the supply of electricity from the national grid to households is generally unstable with residential electricity tariff skyrocketing. Consumers are unable to quantify the amount of electricity purchased in kwh/month in order to appreciate the tariff band they belong to. The billing authorities only account for the approximate unit (kwh/month) for the nearest cedi spent on electricity. The run-offs lead to over/under-billing of consumers for electricity purchased and, thus, derail the agenda of efficient energy management. Given this background, we proceeded to investigate households’ electricity consumption by first, estimating the exact consumption units of electricity for any amount of electricity power purchased. Subsequently, we explored factors that affected households’ electricity consumption in Ghana. We relied on 8,599 households’ data captured in the Ghana Living Standard Survey 7. The method of maximum likelihood estimation was used to estimate a multiple regression model fitted to the monthly electricity consumption data. The results showed that the fewer are alternative sources of electricity available to households, the greater is the consumption of electricity by the households. Other significant variables found were number of rooms in a household, area (m2) of the house, number of people in the house, categories of tariff bands and number of electrical appliances in use. These variables showed positive relationship with the natural log of the estimated electricity consumed per month for any household.

Implications of the COVID-19 Pandemic on China's Power Consumption: A comprehensive Analysis Derived from Monthly Electricity Consumption Forecasts

The COVID-19 pandemic profoundly altered the total and pattern of electricity consumption across Chinese industries and households. However, current research has not fully and accurately measured the impact of the pandemic. This paper builds a novel discrete gray seasonal model (referred to as the FDDGSM model) to predict the scenario of different domestic electricity-consuming sectors and residential electricity consumption without a pandemic backdrop, by comparing it with the actual electricity consumption in 2020, the impact of the COVID-19 pandemic on China's electricity consumption is better revealed. Through empirical analysis, the following conclusions are reached in this study: (1) The novel discrete grey season model shows more stable and accurate characteristics in forecasting time series with seasonal trends. (2) The pandemic did not significantly impact electricity consumption in primary industries and households, while actual usage in secondary and tertiary industries was significantly lower than expected in a non-pandemic scenario. (3) The impact on secondary and tertiary industries varied in magnitude and duration, and they differed in the rate of recovery in electricity consumption.

Energy efficiency management according to ISO 50001: A case study in the brick industry

This research presents the methodology and results of implementing energy efficiency management in the brick industry, given the problem of high electricity consumption in the production processes. Based on the ISO 50001 standard, energy efficiency management has as its structure the PHVA methodology of the Deming cycle and indicators that meet the standard's requirements. Energy consumption in tons of bricks produced is established as an indicator, allowing proposals for improving performance and efficient energy use, as well as implementing a management system, minimizing energy waste, and implementing engineering tools in the processes. Energy consumption data were collected before and after implementation, these data were analyzed, and the decrease in monthly electricity consumption was verified through a pre-test conducted at the beginning of the research, recording parameters of 543,800 kWh. After implementation, a post-test was conducted, recording parameters of 500,296 kWh, resulting in a saving of 43,504 kWh; in monetary units, the saving is S/18,067.21 for each month of production. Obtaining an annual decrease of 522,048 kWh, represented in monetary units S/216,806.53 (US$ 59,891.30 exchange rate S/3.62). Therefore, it is proven that implementing the methodology is feasible through the management of energy efficiency based on ISO 50001 and contributes strategically to the brick industry by increasing the efficiency associated with the reduction of 8% monthly electricity consumption.

Machine Learning and Bagging to Predict Midterm Electricity Consumption in Saudi Arabia

Electricity is widely regarded as the most adaptable form of energy and a major secondary energy source. However, electricity is not economically storable; therefore, the power system requires a continuous balance of electricity production and consumption to be stable. The accurate and reliable assessment of electrical energy consumption enables planning prospective power-producing systems to satisfy the expanding demand for electrical energy. Since Saudi Arabia is one of the top electricity consumers worldwide, this paper proposed an electricity consumption prediction model in Saudia Arabia. In this work, the authors obtained a never-before-seen dataset of Saudi Arabia’s electricity consumption for a span of ten years. The dataset was acquired solely by the authors from the Saudi Electrical Company (SEC), and it has further research potential that far exceeds this work. The research closely examined the performance of ensemble models and the K* model as novel models to predict the monthly electricity consumption for eighteen service offices from the Saudi Electrical Company dataset, providing experiments on a new electricity consumption dataset. The global blend parameters for the K* algorithm were tuned to achieve the best performance for predicting electricity consumption. The K* model achieved a high accuracy, and the results of the correlation coefficient (CC), mean absolute percentage error (MAPE), root mean squared percentage error (RMSPE), mean absolute error (MAE), and root mean squared error (RMSE) were 0.9373, 0.1569, 0.5636, 0.016, and 0.0488, respectively. The obtained results showed that the bagging ensemble model outperformed the standalone K* model. It used the original full dataset with K* as the base classifier, which produced a 0.9383 CC, 0.1511 MAPE, 0.5333 RMSPE, 0.0158 MAE, and 0.0484 RMSE. The outcomes of this work were compared with a previous study on the same dataset using an artificial neural network (ANN), and the comparison showed that the K* model used in this study performed better than the ANN model when compared with the standalone models and the bagging ensemble.

An Overview of Forecasting Methods for Monthly Electricity Consumption

An Overview of Forecasting Methods for Monthly Electricity Consumption

Hybrid Cooling-Based Thermal Management of Containerised Vanadium Flow Battery Systems in Photovoltaic Applications

The integration of industrial batteries with photovoltaic applications is a common practice to charge the batteries using solar energy. Long-duration flow batteries are useful in dealing with the intermittency of renewable energy sources and offer a great opportunity for total fossil fuel replacement. In this study, the effects of different battery operation time and load profiles on the temperature dynamics of a containerised vanadium flow battery system are modelled and simulated for a range of locations and seasons to identify active cooling or heating requirements that might be needed to maintain safe operating temperatures. This paper explores and analyses the stack, tank, and container temperature dynamics of 6 h and 8 h containerised vanadium flow batteries (VFBs) during periods of higher charge and discharge current using computer simulations that apply insulation with passive or active hybrid cooling thermal management where needed to keep the battery temperature within a safe operating range under a range of climate conditions. According to the simulation results, when adopting the hybrid cooling strategy as described in the case study, for a 30 kW–240 kWh VFB system with ambient temperatures fluctuating between 25 °C and 45 °C, the monthly electricity consumption of the air conditioning system, calculated using average power, can be maintained at a relatively low level of approximately 330 kWh. By employing an air conditioning system with an airflow rate of 0.2 m3/s and a suitable thermal management strategy, it is sufficient to keep an 8 h system operating within a safe temperature range when the ambient temperature is between 15 °C and 35 °C. This study presents the first application of our previously developed containerised VFB thermodynamic model to explore the necessity of active cooling or heating in PV (photovoltaic) applications across different geographical locations and seasons. This analysis provides valuable insights for battery designers and manufacturers to understand the performance of containerised battery systems under various climate conditions. Furthermore, this paper is the first to apply this model for simulating 6 and 8 h batteries and to adopt a hybrid thermal management strategy. The simulation data offer guidance on whether active cooling or heating is required for industrialised vanadium batteries with capacities exceeding 6 h.

Does electricity price reduction bring a sustainable development of business: Evidence from fine-grained industrial electricity consumption data in China

This paper studies the impact of electricity price reduction policy on the economic activities and carbon emissions of different industries. Since 2018, the Chinese government has advocated reducing electricity price for industrial customers in order to alleviate the non-tax burden on businesses. Using monthly electricity consumption and user data from 2016 to 2019, we find that the electricity price reduction has significantly increased the industrial electricity consumption and users. Heterogeneity analyses show that the effect is greater in heavy polluted industries and industries in low and medium GDP regions. The results of threshold impact analysis show that the threshold value of heavy pollution industries was lower than that of general industry and commerce, while heavy pollution industries are more sensitive to electricity prices than general industries. Moreover, the reduction of electricity prices results in a higher percentage of carbon emissions growth in heavy pollution industries. Our result can also help the policymakers access the costs and benefits of electricity price reduction for industrial customers more accurately.

Assessment of Transport, Natural and Climatic Factors Influence on the Level of Electric Buses Energy Consumption in Urban Environment

The paper considers the issue of expanding the use of electric buses when transporting passengers on regular urban routes, which contribute to a significant reduction in the overall environmental pollution by road transport. Legislative actsconfirming the state support for the development of this industry are analyzed. The results of driving cycles of electric buses AKСM-E321 [AKSM-E321] and AKСM-E433 [AKSM-E433] in Minsk, electric buses ЛиАЗ-6274.00 [LiAZ-6274.00] and КАМАЗ-6282 [KAMAZ-6282] in Tyumen and Moscow are presented. The correlation of the specific average monthly electricity consumption and the average monthly ambient temperature is revealed. It has been established that for Moscow, the most significant factor influencing the electric bus power consumption are transport conditions. The ambient air temperature was identified as a key factor for the city of Tyumen. The research results make it possible to plan the use of electric buses on various urban routes based on runs without recharging. This may allow us to develop recommendations for the use of the studied brands of electric buses on routes of a certain length and complexity.

Time-Varying approaches for Long-Term Electric Load Forecasting under economic shocks

Long-term Load Forecasting (LTLF) plays a vital role in the planning of electric utilities. In the long run, utilities face various uncertainties caused by economic and environmental factors. These uncertainties have made LTLF more complex and inaccurate, thus, amplifying financial risks of utilities. A potent contributor to such losses in LTLF accuracy is economic shocks. This study proposes two probabilistic Time-Varying (TV) approaches to capture such shocks in LTLF and minimise accuracy loss, namely TV-XGB-X and TV-PR, and their combinations considering economic policy uncertainty. Both eXtreme Gradient Boosting (XGB) and Polynomial Regression (PR) are extended to include the long-run TV effects of economic shocks as eXogeneous predictors in this paper. These models and their combinations are compared with various non-TV approaches through experiments on the monthly electricity consumption of eight energy-intensive states in the United States. The results reveal that the proposed combined approaches outperform stand-alone models on all datasets. The findings of this study can help utilities in hedging financial risks under shocks.

Spatio-temporal Granularity Co-optimization Based Monthly Electricity Consumption Forecasting

Spatio-temporal Granularity Co-optimization Based Monthly Electricity Consumption Forecasting

УПРАВЛЕНИЕ ЭЛЕКТРОПОТРЕБЛЕНИЕМ ПРЕДПРИЯТИЯ ПО ПРОИЗВОДСТВУ МЕДИ

The paper considers the issues of managing the power consumption of an enterprise for the production of copper. The solution of these issues was carried out during the instrumental examination of the power supply system (SES) of the enterprise based on the results of a comprehensive energy audit. An analysis was made of the dynamics of electricity consumption for 2015-2018. It has been established that the uniformity of electricity consumption for production is maintained. The average monthly electricity consumption for 2017 was: for technology - 39,637 thousand kWh; for general shop expenses (general plant) - 6942 thou- sand kWh; in general, for production - 46579 thousand kWh. The total electricity consumption for production by product type is presented, taking into account the auxiliary consumption of all sections for the period 2015-2017. It was found that, compared with 2015, electricity consumption in 2017 for the most energy-intensive processes for electrolytic and blister copper and decreased by 6 % and 1.5 %, respectively, at the same time, an increase in consumption was observed for the production of technical sulfur, in 2016 by 7 %, in 2017 by 15 %...