Analysis of Selected Service Industries in Terms of the Use of Photovoltaics before and during the COVID-19 Pandemic

Previous analyses of the PV market (and the impact of the pandemic on it) have focused on the market as a whole. The literature does not contain analyses of selected services sectors (e.g., catering, hotel services) in terms of the use of photovoltaics. There are no studies that would show in which segments the demand profile for electricity most closely matches the production from photovoltaic installations (not to mention the impact of the pandemic). The authors analyzed selected service sectors (catering and hotel) in terms of the use of photovoltaics before and during the COVID-19 pandemic. The paper proposes a comparative methodology for the use of photovoltaics for self-consumption, including statistical analyses and calculations of the self-consumption index for representatives of various selected services sectors. The highest value of the self-consumption ratio at the level of 52% was shown for cafes and restaurants (during the pandemic). Surprisingly, in the pandemic, the self-consumption rate increased for restaurants and cafes for the same size of installations (compared to pre-pandemic times).

Tunicate swarm algorithm-trained multi-layered perceptron for data centre energy demand forecasting and relative percentage contribution analysis of input parameters

Purpose Energy management is critical to data centres (DCs) majorly because they are high energy-consuming facilities and demand for their services continue to rise due to rapidly increasing global demand for cloud services and other technological services. This projected sectoral growth is expected to translate into increased energy demand from the sector, which is already considered a major energy consumer unless innovative steps are used to drive effective energy management systems. The purpose of this study is to provide insights into the expected energy demand of the DC and the impact each measured parameter has on the building's energy demand profile. This serves as a basis for the design of an effective energy management system. Design/methodology/approach This study proposes novel tunicate swarm algorithm (TSA) for training an artificial neural network model used for predicting the energy demand of a DC. The objective is to find the optimal weights and biases of the model while avoiding commonly faced challenges when using the backpropagation algorithm. The model implementation is based on historical energy consumption data of an anonymous DC operator in Cape Town, South Africa. The data set provided consists of variables such as ambient temperature, ambient relative humidity, chiller output temperature and computer room air conditioning air supply temperature, which serve as inputs to the neural network that is designed to predict the DC’s hourly energy consumption for July 2020. Upon preprocessing of the data set, total sample number for each represented variable was 464. The 80:20 splitting ratio was used to divide the data set into training and testing set respectively, making 452 samples for the training set and 112 samples for the testing set. A weights-based approach has also been used to analyze the relative impact of the model’s input parameters on the DC’s energy demand pattern. Findings The performance of the proposed model has been compared with those of neural network models trained using state of the art algorithms such as moth flame optimization, whale optimization algorithm and ant lion optimizer. From analysis, it was found that the proposed TSA outperformed the other methods in training the model based on their mean squared error, root mean squared error, mean absolute error, mean absolute percentage error and prediction accuracy. Analyzing the relative percentage contribution of the model's input parameters based on the weights of the neural network also shows that the ambient temperature of the DC has the highest impact on the building’s energy demand pattern. Research limitations/implications The proposed novel model can be applied to solving other complex engineering problems such as regression and classification. The methodology for optimizing the multi-layered perceptron neural network can also be further applied to other forms of neural networks for improved performance. Practical implications Based on the forecasted energy demand of the DC and an understanding of how the input parameters impact the building's energy demand pattern, neural networks can be deployed to optimize the cooling systems of the DC for reduced energy cost. Originality/value The use of TSA for optimizing the weights and biases of a neural network is a novel study. The application context of this study which is DCs is quite untapped in the literature, leaving many gaps for further research. The proposed prediction model can be further applied to other regression tasks and classification tasks. Another contribution of this study is the analysis of the neural network's input parameters, which provides insight into the level to which each parameter influences the DC’s energy demand profile.

A bi-directional approach to building-integrated PV systems configuration

The configuration of local building-integrated photovoltaic (PV) installations can benefit from computational support. Especially in cases where a high degree of energy self-sufficiency is desired, it is important to optimally match the temporal profiles of the building’s energy demand and the available solar radiation intensity. Typically, the building’s demand profile is taken as given, which is treated as the basis for the sizing and configuration of the PV installation. The computational approach framework introduced in this paper is intended to offer additional functionalities. Specifically, it is conceived to facilitate a bi-directional approach to supporting the design and configuration of PV installations. This approach not only informs the configuration of PV system based on the building’s demand profile, but also allows for the exploration of the consequences of the magnitude and temporal profile of the PV’s energy supply potential for the values of relevant building design variables (e.g., building orientation, fraction of glazing in the envelope). The paper presents this computational approach and its functionality in terms of an illustrative case study.

Research on Game Operation of Multi-Stakeholder in Distribution Network in Electricity Market

With the development of the electricity market, various stakeholders such as batteries, multi-microgrid (MMG), and electric vehicle (EV) clusters, can trade with either the distribution network or each other to meet their power balance needs and to maximize their profits. This paper proposes a two-level game model based on game theory to study the operation strategy of stakeholders in the distribution network. First, each stakeholder predicts its electricity demand profile. A Markov Decision Process (MDP) model of random variables is established to predict the charging and discharging power of the battery. Then, the two-level game is presented to let multi-stakeholder participate, in which different kinds of stakeholders have different game strategy limits. Additionally, suggestions for battery operation modes under different compensation coefficients are given to participate in the subsequent two-level game. An algorithm is proposed to allow stakeholders to merge or split self-adaptively based on Nondominated Sorting Genetic Algorithm II (NSGA-II) to optimize operation mode. Finally, the proposed model is applied to the PG and E69-bus distribution system and a practical 101-bus distribution system in China. The case studies show that different game strategy limits of the stakeholders will affect the distribution of the Nash equilibrium (NE) solutions. The multi-stakeholder system can better absorb regional unbalanced power through electricity transactions, and further increase the benefits of each stakeholder.

Evaluation of driver visual demand at different design speeds on complex two-dimensional rural highway alignments

Road crashes are a major cause of loss of human life, property and money throughout the world. One of the reasons behind these crashes is the interaction between drivers and road alignments. The need to understand the factors that affect drivers has become obvious and is now being addressed by researchers. Moreover, driver workload is gaining attention as a measure of highway-design consistency as it directly reveals design features to the driver. This research focuses on evaluating driver visual demand at different design speeds along with other geometric design features for two-dimensional rural horizontal roadway alignments. Twelve such alignments having simple and complex curves were designed following the standards of the American Association of Highway and Transportation Officials (AASHTO) and the Transportation Association of Canada (TAC). The driver simulator at Ryerson University, Toronto, recently modified after the integration of a car, was used for the simulation of roadway alignments. Scenario Definition Language (SDL) was used to develop Event files for simulation and to save the required data. Twelve drivers drove the simulated alignments. The output data relating to driver visual demand were processed using MS Notepad and MS Excel. The visual demand calculations for full-element length (VDF), half-element length (VDH) and the first 30 m of element length (VD30) for curve and tangent sections of alignments were done using MS Excel. Statistical Analysis Software (SAS) was used to anlayze and develop models for VDF, VDH and VD30 for curve and tangent sections, first considering design speed only as explanatory variable and then considering design speed along with other geometric design characteristics as explanatory variables. It has been observed that visual demand increases with the increase in design speed. Besides, the combined effect of design speed an other geometric design characteristics (e.g., the type of preceding element, the turning direction of a curve) has significant effect on visual demand. It was also found that visual demand followed a Log Normalized distribution which was also observed by previous research. The developed models were used to establish the visual demand profile for highway design consistency evaluation. The comparison of visual demand profile and operating speed profile has shown that the visual demand can be an acceptable measure for evaluating the highway design consistency.

Evaluation of driver visual demand at different design speeds on complex two-dimensional rural highway alignments

Road crashes are a major cause of loss of human life, property and money throughout the world. One of the reasons behind these crashes is the interaction between drivers and road alignments. The need to understand the factors that affect drivers has become obvious and is now being addressed by researchers. Moreover, driver workload is gaining attention as a measure of highway-design consistency as it directly reveals design features to the driver. This research focuses on evaluating driver visual demand at different design speeds along with other geometric design features for two-dimensional rural horizontal roadway alignments. Twelve such alignments having simple and complex curves were designed following the standards of the American Association of Highway and Transportation Officials (AASHTO) and the Transportation Association of Canada (TAC). The driver simulator at Ryerson University, Toronto, recently modified after the integration of a car, was used for the simulation of roadway alignments. Scenario Definition Language (SDL) was used to develop Event files for simulation and to save the required data. Twelve drivers drove the simulated alignments. The output data relating to driver visual demand were processed using MS Notepad and MS Excel. The visual demand calculations for full-element length (VDF), half-element length (VDH) and the first 30 m of element length (VD30) for curve and tangent sections of alignments were done using MS Excel. Statistical Analysis Software (SAS) was used to anlayze and develop models for VDF, VDH and VD30 for curve and tangent sections, first considering design speed only as explanatory variable and then considering design speed along with other geometric design characteristics as explanatory variables. It has been observed that visual demand increases with the increase in design speed. Besides, the combined effect of design speed an other geometric design characteristics (e.g., the type of preceding element, the turning direction of a curve) has significant effect on visual demand. It was also found that visual demand followed a Log Normalized distribution which was also observed by previous research. The developed models were used to establish the visual demand profile for highway design consistency evaluation. The comparison of visual demand profile and operating speed profile has shown that the visual demand can be an acceptable measure for evaluating the highway design consistency.

Lesotho electricity demand profile from 2010 to 2030

This study undertook a 2010 to 2030 electricity demand profile for Lesotho, with 2010 used as the base year. The demand forecast was modelled using the International Atomic Energy Agency Model for Analysis of Energy Demand, largely because of its proven ability to accurately forecast demand in developing economies based on socio-economic, technology and demography variables. The model correlates well with the actual data, where data exists, and predicts that by 2030 Lesotho will achieve a national electrification rate of 54.2%, with 95% for urban households and 14% for rural households, up from 19.4%, 54.1% and 1.8% respectively in the base year. Moreover, in the same period, the forecast for the most likely scenario gives the following results: the maximum demand will increase to 211 MW from 121 MW; the annual average household energy consumption will continue its decline to 1 009 kWh/household from 1 998 kWh/household; and the total consumption will increase to 1 128 284 MWh from 614 868 MWh. The overall low growth rate is attributed to the consistently declining average household consumption that is contrary to international norms. The forecast results gave a root mean square percentage error of 1.5% and mean absolute percentage error of 1.3%, which implied good correlation with the actual data and, hence, confidence in the accuracy of the results. Highlights Between 2030 and 2010: Achievement of national electrification rate of 54.2% up from 19.4%. Electrification: 95% urban, 14% rural households, from 54.1% and 1.8% respectively. The maximum demand will increase to 211 MW from 121 MW. Annual average household consumption will decline to 1 009 kWh/household from 1,998 kWh/household

Future of Business Intelligence in Cloud Computing

Cloud business intelligence can solve numerous management issues that are faced by many businesses. If it is used in a correct manner, it can substitute seamless utilization of crucial information in the growth of business. In the self-hosted environment, business intelligence will face resource crisis situation on the never-ending expansion of warehouses and OLAP's demands on the primary network. Today, cloud computing has instigated optimism for the prospects of future business intelligence. But thing to focus here is, how will business intelligence be implemented on cloud platform, and further, how will the traffic be managed and what will the demand profile look like? Moreover, in today's world, data generated on a daily basis from many different sources are numerous and valuable information for making effective decisions. This chapter focuses and tries to attempt these questions related to taking business intelligence to the cloud.

Design Procedure of a Hybrid Renewable Power Generation System

Electrification of small communities in districted off-grid area remains as a challenge for power generation industries. In the current study, various aspects of design of a standalone renewable power plant are examined and implemented in a case study of a rural area in Cape Town, South Africa. Estimating required electricity based on local demand profile, investment, operability, and maintenance costs of different generation technologies are studied in order to investigate their potential in an off-grid clean energy generation system. Several configurations of hybridization of solar system, wind, and micro gas turbine in combination with a battery are investigated. The Levelized Cost of Electricity (LCOE) and number of days with more than 3 h black out are compared.