Search results

An unreliable supply of grid electricity has a strong negative impact on industrial and commercial profitability as well as on household activities and government services that rely on electricity supply. This unreliable grid electricity could be a result of technical and security factors affecting the grid network. Therefore, this study aims to investigate the effects of technical and security factors on the transmission and distribution of grid electricity in Uganda.

This study used the ordinary least squares (OLS) and autoregressive distributed lag (ARDL) models to examine the effects of technical and security factors on grid electricity reliability in Uganda. The study draws upon secondary time series monthly data sourced from the Uganda Electricity Transmission Company Limited (UETCL) government utility, which transmits electricity to both distributors and grid users. Additionally, data from Umeme Limited, the largest power distribution utility in Uganda, were incorporated into the analysis.

The findings revealed that technical faults, failed grid equipment, system overload and theft and vandalism affected grid electricity reliability in the transmission and distribution subsystems of the Ugandan power grid network. The effect was computed both in terms of frequency and duration of power outages. For instance, the number of power outages was 116 and 2,307 for transmission and distribution subsystems, respectively. In terms of duration, the power outages reported on average were 1,248 h and 5,826 h, respectively, for transmission and distribution subsystems.

This paper investigates the effects of technical and security factors on the transmission and distribution grid electricity reliability, specifically focusing on frequency and duration of power outages, in the Ugandan context. It combines both OLS and ARDL models for analysis and adopts the systems reliability theory in the area of grid electricity reliability research.

On the one hand, this paper is to further understand the residents' differentiated power consumption behaviors and tap the residential family characteristics labels from the perspective of electricity stability. On the other hand, this paper is to address the problem of lack of causal relationship in the existing research on the association analysis of residential electricity consumption behavior and basic information data.

First, the density-based spatial clustering of applications with noise method is used to extract the typical daily load curve of residents. Second, the degree of electricity consumption stability is described from three perspectives: daily minimum load rate, daily load rate and daily load fluctuation rate, and is evaluated comprehensively using the entropy weight method. Finally, residential customer labels are constructed from sociological characteristics, residential characteristics and energy use attitudes, and the enhanced FP-growth algorithm is employed to investigate any potential links between each factor and the stability of electricity consumption.

Compared with the original FP-growth algorithm, the improved algorithm can realize the excavation of rules containing specific attribute labels, which improves the excavation efficiency. In terms of factors influencing electricity stability, characteristics such as a large number of family members, being well employed, having children in the household and newer dwelling labels may all lead to poorer electricity stability, but residents' attitudes toward energy use and dwelling type are not significantly associated with electricity stability.

This paper aims to uncover household socioeconomic traits that influence the stability of home electricity use and to shed light on the intricate connections between them. Firstly, in this article, from the perspective of electricity stability, the characteristics of the power consumption of residents' users are refined. And the authors use the entropy weight method to comprehensively evaluate the stability of electricity usage. Secondly, the labels of residential users' household characteristics are screened and organized. Finally, the improved FP-growth algorithm is used to mine the residential household characteristic labels that are strongly associated with electricity consumption stability.

1. The stability of electricity consumption is important to the stable operation of the grid.

2. An improved FP-growth algorithm is employed to explore the influencing factors.

3. The improved algorithm enables the mining of rules containing specific attribute labels.

4. Residents' attitudes toward energy use are largely unrelated to the stability of electricity use.

The stability of electricity consumption is important to the stable operation of the grid.

An improved FP-growth algorithm is employed to explore the influencing factors.

The improved algorithm enables the mining of rules containing specific attribute labels.

Residents' attitudes toward energy use are largely unrelated to the stability of electricity use.

For some years now, Cameroon has seen a significant increase in its electricity demand, and this need is bound to grow within the next few years owing to the current economic growth and the ambitious projects underway. Therefore, one of the state's priorities is the mastery of electricity demand. In order to get there, it would be helpful to have reliable forecasting tools. This study proposes a novel version of the discrete grey multivariate convolution model (ODGMC(1,N)).

Specifically, a linear corrective term is added to its structure, parameterisation is done in a way that is consistent to the modelling procedure and the cumulated forecasting function of ODGMC(1,N) is obtained through an iterative technique.

Results show that ODGMC(1,N) is more stable and can extract the relationships between the system's input variables. To demonstrate and validate the superiority of ODGMC(1,N), a practical example drawn from the projection of electricity demand in Cameroon till 2030 is used. The findings reveal that the proposed model has a higher prediction precision, with 1.74% mean absolute percentage error and 132.16 root mean square error.

These interesting results are due to (1) the stability of ODGMC(1,N) resulting from a good adequacy between parameters estimation and their implementation, (2) the addition of a term that takes into account the linear impact of time t on the model's performance and (3) the removal of irrelevant information from input data by wavelet transform filtration. Thus, the suggested ODGMC is a robust predictive and monitoring tool for tracking the evolution of electricity needs.

Electricity plays an essential role in nations' economic development. However, coal and renewables currently play an important part in electricity production in major world economies. The current study aims to forecast the electricity production from coal and renewables in the USA, China and Japan.

Two intelligent grey forecasting models – optimized discrete grey forecasting model DGM (1,1,α), and optimized even grey forecasting model EGM (1,1,α,θ) – are used to forecast electricity production. Also, the accuracy of the forecasts is measured through the mean absolute percentage error (MAPE).

Coal-powered electricity production is decreasing, while renewable energy production is increasing in the major economies (MEs). China's coal-fired electricity production continues to grow. The forecasts generated by the two grey models are more accurate than that by the classical models EGM (1,1) and DGM (1,1) and the exponential triple smoothing (ETS).

The study confirms the reliability and validity of grey forecasting models to predict electricity production in the MEs.

The purpose of this paper is to examine how active consumers, i.e. consumers that can inter-temporally shift their load, can influence electricity prices. As demonstrated in this paper, inter-temporal load shifting can induce negative electricity prices, a recurring phenomenon on power exchanges.

The paper presents a novel electricity-market model assuming a nodal-pricing, energy-only spot market with active consumers. This study formulates an economic equilibrium problem as a linear program and uses an established six-node case study to compare equilibrium prices of a model with inflexible demand to a model with flexible demand of active consumers.

This study illustrates that temporal coupling of hourly market clearing through load shifting of active consumers can cause negative electricity prices that are not observed in a model with ceteris paribus inflexible demand. In such situations, where compared to the case of inflexible demand more flexibility is available in the system, negative electricity prices signal lower total system costs. These negative prices result from the use of demand flexibility, which, however, cannot be fully exploited due to limited transmission capacities, respectively, loop-flow restrictions.

Literature indicates that negative electricity prices result from lacking flexibility. The results illustrate that active consumers and their additional flexibility can lead to negative electricity prices in temporally coupled markets, which in general contributes to increased system efficiency as well as increased use of renewable energy sources. These findings extend existing research in both the area of energy flexibility and causes for negative electricity prices. Therefore, policymakers should be aware of such (temporal coupling) effects and, e.g. continue to allow negative electricity prices in the future that can serve as investment signals for active consumers.

The energy efficiency of buildings has been emphasized along with the continual development in the building and construction sector that consumes a significant amount of energy. To this end, the purpose of this research paper is to forecast energy consumption to improve energy resource planning and management.

This study proposes the application of the convolutional neural network (CNN) for estimating the electricity consumption in the Grey Nuns building in Canada. The performance of the proposed model is compared against that of long short-term memory (LSTM) and multilayer perceptron (MLP) neural networks. The models are trained and tested using monthly electricity consumption records (i.e. from May 2009 to December 2021) available from Concordia’s facility department. Statistical measures (e.g. determination coefficient [R²], root mean squared error [RMSE], mean absolute error [MAE] and mean absolute percentage error [MAPE]) are used to evaluate the outcomes of models.

The results reveal that the CNN model outperforms the other model predictions for 6 and 12 months ahead. It enhances the performance metrics reported by the LSTM and MLP models concerning the R², RMSE, MAE and MAPE by more than 4%, 6%, 42% and 46%, respectively. Therefore, the proposed model uses the available data to predict the electricity consumption for 6 and 12 months ahead. In June and December 2022, the overall electricity consumption is estimated to be 195,312 kWh and 254,737 kWh, respectively.

This study discusses the development of an effective time-series model that can forecast future electricity consumption in a Canadian heritage building. Deep learning techniques are being used for the first time to anticipate the electricity consumption of the Grey Nuns building in Canada. Additionally, it evaluates the effectiveness of deep learning and machine learning methods for predicting electricity consumption using established performance indicators. Recognizing electricity consumption in buildings is beneficial for utility providers, facility managers and end users by improving energy and environmental efficiency.

This paper aims to examine the relationship between the payment of bribes, the access to electricity and the productivity of informal production units (IPUs).

The data used for this study come from the second Survey on Employment and Informal Sector conducted in 2010 by the National Institute of Statistics of Cameroon and representative at the national level. The survey was conducted among 3,560 IPUs. Survey participants reported whether they had been personally affected by corruption in the twelve months preceding the survey. Relying on the data of this survey, the recursive trivariate probit model was used to study the correlation between corruption and access to electricity.

The results reveal that the payment of bribes positively influences IPU access to electricity, and consequently access to this infrastructure has a positive impact on company performance.

A main limitation of this paper is the environment of study in which corruption appeared to be institutionalised. It would therefore be interesting to extend the results obtained by conducting research in other countries and also including other infrastructures such as telecommunications.

The main contribution of this research is to highlight the effectiveness of the fight against corruption and its impact on the access of some basics resources that affect the performance of certain companies. Indeed, the fight against corruption would be easier if economic actors had access to certain resources and fundamental infrastructures for their activities. Thus, improving the supply of resources and infrastructures can be an important lever in the fight against corruption in Africa.

This research addresses a vulnerable sector vis-à-vis the pressure of the actors involved in the provision of a service essential to the activity of companies. It highlights the justification for accepting the use of corruption. Indeed, entrepreneurs are faced with a dilemma between moral standards on the one hand, and economic imperatives on the other. If corruption is a condition of access to electricity which, in turn, improves performance, it is easy to pay bribes to gain access to electricity.

This study aims to examine the price and income elasticities of short- and long-run industrial electricity demand in Brazil between 2003 and 2020. The research also examines the impacts of COVID-19 in Brazil’s industrial electricity sector, including an analysis in states more and less industrialized.

Dynamic adjustments models in panel data are used to present robust estimates and analyze the impact of different methodologies on reported elasticities.

The short-run price elasticity is estimated at −0.448, while the long-run values are around −1.60. Regarding income elasticity, the value is 0.069 in the short-run and is concentrated in 0.25 in the long-run. The inelastic results of income show that the industrial demand for electric energy follows the trend of loss of competitiveness of the Brazilian industry in the past years. In addition, the price of natural gas, the level of employment, and, in specific cases, the level of imports also influence industrial electricity demand.

The research is a pioneer in the investigation of the industrial behavior of electricity of the Brazilian industrial branch, using as control variables, the average temperature, and the level of rainfall, this one, so important for a country whose main source is hydroelectric. In addition, to the best of the authors’ knowledge, it is the first study, which is prepared to analyze the effects of COVID-19 on electric consumption in the industrial sector, investigating these impacts, including in the states considered more and less industrialized. The estimates generated may help in the design of the Brazilian energy policy.

This study aims to provide empirical evidence on two research questions: firstly, whether green finance is positively related to electricity access, and, secondly, if the domestic economic environment moderates the relationship between green finance and electricity access? This paper pays particular attention to the regional disparities in Africa.

While pursuing the study objectives, the authors apply a variety of statistical approaches and tools to assess the robustness of the findings. The authors use panel dataset for analysing data. In order to empirically examine the relationship between green finance and electricity access in the African region, the paper employs static and dynamic panel estimation methods, Poisson method and adopts two-step system generalized method of moments (GMM) approach for dealing with issues relating to endogeneity. The authors also use alternate proxy for the electricity access, which is drawn from the regulatory indicators for sustainable energy (RISE) scores.

The authors find that despite the fact that green funding appears to support job creation, household incomes aren't high enough to drive rising demand for electricity. The study underscores the role and responsibilities of external funding agencies to ensure that funds at the receiving end are effectively routed to encourage access to clean and sustainable energy, which is good to the economic and domestic environment. Further, due to the relatively modest size of some funds, the cost to administer those funds is larger than the funds themselves. This causes inefficiencies, which may temporarily provide jobs but not lasting growth. This means there is no regular need for energy, therefore larger investors have no reason to enter the market. This discourages investors from public-private partnerships or private investments and prevents future investment.

The provide insights into the private-public partnerships and whether the challenges to electricity access are being turned into investment opportunities. The effects of the power Africa project initiatives are revealing, with, sanitation being an impediment to the development of electricity infrastructure, specifically in low-income group countries.

The study confirms the view that trivial amounts of green financing (US-Aid or grants) impose a burden on the absorptive capacity of the recipient government and increases the transaction costs and is likely to be an impediment (Kimura et al., 2012) to initiating projects that enhance electricity access.

The results indicate that although green financing seems to be supporting employment opportunities, income levels are insufficient to create demand for electricity usage. It, therefore, becomes imperative that sanitation (SDG 6) is fully addressed in order to ensure that SDG 7 is attained.

The authors provide insights around the private public partnerships and whether the challenges to electricity access are being turned into investment opportunities. The effects of the power Africa project initiatives are revealing, with, sanitation being an impediment to the development of electricity infrastructure, specifically in low-income group countries.

The current wave of decreasing electricity supply to meet the immediate demand of the populace is influencing not only economic growth but also the industrial productivity of the ECOWAS sub-region. In this context, this paper investigates the long-run and causal relationships between electricity consumption and industrial output in selected ECOWAS countries over the period 1971–2017.

The Autoregressive Distributed Lag (ARDL) bound testing approach is employed to determine the existence of relationships among the variables. The causal nexus between electricity consumption and industrial output is examined using both the Toda-Yamamoto causality test and the bootstrap-corrected causality technique.

The long run results indicated that increasing electricity supply enhances industrial output only in Benin, Cote d'Ivoire, Gambia, Guinea, Liberia, Nigeria, Senegal, and Sierra Leone. Furthermore, the causality test results confirmed the presence of all four hypotheses in this study, but the two causality tests agree, particularly in the evidence of growth and neutrality hypotheses. In the cases of Benin, Burkina Faso, Gambia, Ghana, Nigeria, and Sierra Leone, a unilateral causality running from electricity consumption to industrial output is found. However, no evidence of causality between electricity consumption and industrial production has been confirmed in Cote d'Ivoire, Guinea Bissau, Liberia and Niger.

The relevant energy stakeholders in the subregion need to reprioritize their policy framework to focus more on the electricity sector of their economies since electricity consumption is identified as an important driver of industrial growth in the West African countries.

This is the first study to provide a comparative and country-specific investigation of the nexus between electricity consumption and industrial output in Africa, particularly in the West African region.