65 research outputs found
A Review of Electricity Demand Forecasting in Low and Middle Income Countries: The Demand Determinants and Horizons
With the globally increasing electricity demand, its related uncertainties are on the rise as well. Therefore, a deeper insight of load forecasting techniques for projecting future electricity demands becomes imperative for business entities and policy makers. The electricity demand is governed by a set of different variables or “electricity demand determinants”. These demand determinants depend on forecasting horizons (long term, medium term, and short term), the load aggregation level, climate, and socio-economic activities. In this paper, a review of different electricity demand forecasting methodologies is provided in the context of a group of low and middle income countries. The article presents a comprehensive literature review by tabulating the different demand determinants used in different countries and forecasting the trends and techniques used in these countries. A comparative review of these forecasting methodologies over different time horizons reveals that the time series modeling approach has been extensively used while forecasting for long and medium terms. For short term forecasts, artificial intelligence-based techniques remain prevalent in the literature. Furthermore, a comparative analysis of the demand determinants in these countries indicates a frequent use of determinants like the population, GDP, weather, and load data over different time horizons. Following the analysis, potential research gaps are identified, and recommendations are provided, accordingly
Fuzzy clustering means algorithm analysis for power demand prediction at PT PLN Lhokseumawe
Indonesian National Electricity Company (PT PLN) as the main electric power provider in Lhokseumawe City. In fulfilling the need of electricity supply for the whole requirement, which upscale gradually. The proper forecasting method need to be premeditated. The area that was grouped based on the total of power consists of the four sub districts, namely Banda Sakti, Blang Mangat, Muara Dua and Muara Satu. In this study the fuzzy clustering mean (FCM) Classification was applied in determining the power demand of each area and categorized into a cluster respectively. The data clustering divided into six variable and five classifications of power of customer. Based on clustering step that applied revealed for four different classification of power requirement for future demand, the house hold electricity consumption measured for current consumption 9.588.466 Kw/H and forecast 10.037.248 Kw/H, for Business cluster classification measured 10.107.845 Kw/H and forecast 10.566.854 Kw/H, for industry the power measured 9.195.027 Kw/H and the forecasting revealed 9.638.804 Kw/H, and the last analysis was applied in general cluster classification based on measurement was recorded 9.729.048 Kw/H and forecasted result 10.198.282 Kw/H. this method has shown the better result in term of forecasting method by employing the cluster system in determining future power consumption requirement for the area of Lhokseumawe District
Decarbonization cost of Bangladesh's energy sector: Influence of corruption
As a rapidly developing lower-middle income country, Bangladesh has been maintaining
a steady growth of +5% in the gross domestic product (GDP) annually since
2004, eventually reaching 7.1% in 2016. The country is targeting to become uppermiddle-
income and developed by 2021 and 2041 respectively, which translates to an
annual GDP growth rate of 7.58% during this period. The bulk of this growth
is expected to come from the manufacturing sector, the significant shift towards
which started at the turn of this century. Energy intensity of manufacturing-based
growth is higher, the evidence of which can be seen in the 3.17 times increase in
national energy consumption between 2001 and 2014. Also, Bangladesh aims to
achieve 100% electrification rate by 2021 against an annual population growth rate
of 1.08%. With the increasing per capita income, there is now a growing middle
class fuelling the growth in demand for convenient forms of energy. Considering
the above drivers, the Bangladesh 2050 Pathways Model suggested 35 times higher
energy demand than that of 2010 by 2050. The government and private sector have
started a substantial amount of investments in the energy sector to meet the signi
ficant future demand. Approximately US250 billion in 2050 under HCS, which can be reduced 23% under
ZCS. The cost of decarbonization would be 3.6, 3.4 and 3.2 times under average
cost of MCS, LCS, and ZCS, than that of HCS. As the energy sector of Bangladesh
is under rapid development, the accumulated capital would be comparatively high
by 2050. However, fuel cost can be significantly reduced under LCS and ZCS which
would also ensure lower emissions. The study suggested that energy mix change,
technological maturity, corruption and demand reduction can influence the cost
of decarbonization. However, the most significant influencer for the decarbonization
of Bangladeshi energy sector would be the corruption. Results showed that if
Bangladesh can minimize the effect of corruption on the energy sector, it can reduce
the cost of decarbonization 45-77% by 2050 under MCS, LCS, and ZCS
Forecasting methods in energy planning models
Energy planning models (EPMs) play an indispensable role in policy formulation and energy sector development. The forecasting of energy demand and supply is at the heart of an EPM. Different forecasting methods, from statistical to machine learning have been applied in the past. The selection of a forecasting method is mostly based on data availability and the objectives of the tool and planning exercise. We present a systematic and critical review of forecasting methods used in 483 EPMs. The methods were analyzed for forecasting accuracy; applicability for temporal and spatial predictions; and relevance to planning and policy objectives. Fifty different forecasting methods have been identified. Artificial neural network (ANN) is the most widely used method, which is applied in 40% of the reviewed EPMs. The other popular methods, in descending order, are: support vector machine (SVM), autoregressive integrated moving average (ARIMA), fuzzy logic (FL), linear regression (LR), genetic algorithm (GA), particle swarm optimization (PSO), grey prediction (GM) and autoregressive moving average (ARMA). In terms of accuracy, computational intelligence (CI) methods demonstrate better performance than that of the statistical ones, in particular for parameters with greater variability in the source data. However, hybrid methods yield better accuracy than that of the stand-alone ones. Statistical methods are useful for only short and medium range, while CI methods are preferable for all temporal forecasting ranges (short, medium and long). Based on objective, most EPMs focused on energy demand and load forecasting. In terms geographical coverage, the highest number of EPMs were developed on China. However, collectively, more models were established for the developed countries than the developing ones. Findings would benefit researchers and professionals in gaining an appreciation of the forecasting methods, and enable them to select appropriate method(s) to meet their needs
Advanced Methods of Power Load Forecasting
This reprint introduces advanced prediction models focused on power load forecasting. Models based on artificial intelligence and more traditional approaches are shown, demonstrating the real possibilities of use to improve prediction in this field. Models of LSTM neural networks, LSTM networks with a SESDA architecture, in even LSTM-CNN are used. On the other hand, multiple seasonal Holt-Winters models with discrete seasonality and the application of the Prophet method to demand forecasting are presented. These models are applied in different circumstances and show highly positive results. This reprint is intended for both researchers related to energy management and those related to forecasting, especially power load
Spationomy
This open access book is based on "Spationomy – Spatial Exploration of Economic Data", an interdisciplinary and international project in the frame of ERASMUS+ funded by the European Union. The project aims to exchange interdisciplinary knowledge in the fields of economics and geomatics. For the newly introduced courses, interdisciplinary learning materials have been developed by a team of lecturers from four different universities in three countries. In a first study block, students were taught methods from the two main research fields. Afterwards, the knowledge gained had to be applied in a project. For this international project, teams were formed, consisting of one student from each university participating in the project. The achieved results were presented in a summer school a few months later. At this event, more methodological knowledge was imparted to prepare students for a final simulation game about spatial and economic decision making. In a broader sense, the chapters will present the methodological background of the project, give case studies and show how visualisation and the simulation game works
Spationomy
This open access book is based on "Spationomy – Spatial Exploration of Economic Data", an interdisciplinary and international project in the frame of ERASMUS+ funded by the European Union. The project aims to exchange interdisciplinary knowledge in the fields of economics and geomatics. For the newly introduced courses, interdisciplinary learning materials have been developed by a team of lecturers from four different universities in three countries. In a first study block, students were taught methods from the two main research fields. Afterwards, the knowledge gained had to be applied in a project. For this international project, teams were formed, consisting of one student from each university participating in the project. The achieved results were presented in a summer school a few months later. At this event, more methodological knowledge was imparted to prepare students for a final simulation game about spatial and economic decision making. In a broader sense, the chapters will present the methodological background of the project, give case studies and show how visualisation and the simulation game works
LIPIcs, Volume 277, GIScience 2023, Complete Volume
LIPIcs, Volume 277, GIScience 2023, Complete Volum
Improving solar gain control strategies in residential buildings located in a hot climate (Tripoli-Libya)
A large number of recently-built residential buildings in Libya provide a poor quality indoor environment or require a huge amount of energy to run the air conditioning, therefore influencing the thermal comfort, energy consumption and carbon emissions. As the use of energy in buildings is the major contributor to air pollution and global climate change, improving energy efficiency through the application of bioclimatic design principles in residential buildings in Libya is a critical factor in reducing energy consumption, securing thermal comfort, and hence is an effective policy for reducing the environmental impacts such as global warming and ozone layer depletion.
This research assumes that the use of appropriate orientation, materials and building configuration would offer suitable solutions for energy and environmental problems in hot, arid countries. This hypothesis is examined through an example located in Libya. A domestic building in Libya was studied with a view to reducing its energy consumption. The study included detailed monitoring for 45 days continuously, followed by computer simulation of a range of intervention strategies.
A field study including temperature, humidity and electricity consumption measurements was carried out and results from the study were gathered and analysed. Moreover a computer simulation model was built using IES software, a fully dynamic simulation model to investigate the potential influence of changes to the building.
The thermal comfort of users in a residential building in Tripoli, Libya was investigated. Field measurements and subjective environmental perception survey were used. It was established that building design in hot arid regions must consider thermal requirements
- …