Building energy management systems use important information from indoor room temperature (IRT) forecasting to predict daily loads within smart buildings. IRT forecasting is a…
Abstract
Purpose
Building energy management systems use important information from indoor room temperature (IRT) forecasting to predict daily loads within smart buildings. IRT forecasting is a complex and challenging task, especially when energy demands are exponentially rising. The purpose of this paper is to review the relevant literature on indoor temperature forecasting in the past two decades and draw inferences on important methodologies with influencing variables and offer future directions.
Design/methodology/approach
The motivation for this work is based on the research work done in the field of intelligent buildings and energy related sector. The focus of this study is based on past literature on forecasting models and methodologies related to IRT forecasting for building energy management, with an emphasis on data-driven models (statistical and machine learning models). The methodology adopted here includes review of several journals, conference papers, reference books and PhD theses. Selected forecasting methodologies have been reviewed for indoor temperature forecasting contributing to building energy consumption. The models reviewed here have been earmarked for their benefits, limitations, location of study, accuracy along with the identification of influencing variables.
Findings
The findings are based on 62 studies where certain accuracy metrics and influencing explanatory variables have been reviewed. Linear models have been found to show explanatory relationships between the variables. Nonlinear models are found to have better accuracy than linear models. Moreover, IRT profiles can be modeled with enhanced accuracy and generalizability through hybrid models. Although deep learning models are found to have better performance for this study.
Research limitations/implications
This is accuracy-based study of data-driven models. Their run-time performance and cost implications review and review of physical, thermal and simulation models is future scope.
Originality/value
Despite the earlier work conducted in this field, there is a lack of organized and comprehensive evaluation of peer reviewed forecasting methodologies. Indoor temperature depends on various influencing explanatory variables which poses a research challenge for researchers to develop suitable predictive model. This paper presents a critical review of selected forecasting methodologies and provides a list of important methodologies along with influencing variables, which can help future researchers in the field of building energy management sector. The forecasting methods presented here can help to determine appropriate heating, ventilation and air-conditioning systems for buildings.
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In the context of a developing country, Indian buildings need further research to channelize energy needs optimally to reduce energy wastage, thereby reducing carbon emissions…
Abstract
Purpose
In the context of a developing country, Indian buildings need further research to channelize energy needs optimally to reduce energy wastage, thereby reducing carbon emissions. Also, reduction in smart devices’ costs with sequential advancements in Information and Communication Technology have resulted in an environment where model predictive control (MPC) strategies can be easily implemented. This study aims to propose certain preemptive measures to minimize the energy costs, while ensuring the thermal comfort for occupants, resulting in better greener solutions for building structures.
Design/methodology/approach
A simulation-based multi-input multi-output MPC strategy has been proposed. A dual objective function involving optimized energy consumption with acceptable thermal comfort has been achieved through simultaneous control of indoor temperature, humidity and illumination using various control variables. A regression-based lighting model and seasonal auto-regressive moving average with exogenous inputs (SARMAX) based temperature and humidity models have been chosen as predictor models along with four different control levels incorporated.
Findings
The mathematical approach in this study maintains an optimum tradeoff between energy cost savings and satisfactory occupants’ comfort levels. The proposed control mechanism establishes the relationships of output variables with respect to control and disturbance variables. The SARMAX and regression-based predictor models are found to be the best fit models in terms of accuracy, stability and superior performance. By adopting the proposed methodology, significant energy savings can be accomplished during certain hours of the day.
Research limitations/implications
This study has been done on a specific corporate entity and future analysis can be done on other corporate or residential buildings and in other geographical settings within India. Inclusion of sensitivity analysis and non-linear predictor models is another area of future scope.
Originality/value
This study presents a dynamic MPC strategy, using five disturbance variables which further improves the overall performance and accuracy. In contrast to previous studies on MPC, SARMAX model has been used in this study, which is a novel contribution to the theoretical literature. Four levels of control zones: pre-cooling, strict, mild and loose zones have been used in the calculations to keep the Predictive Mean Vote index within acceptable threshold limits.
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The rapid urbanization of Indian cities and the population surge in cities has steered a massive demand for energy, thereby increasing the carbon emissions in the environment…
Abstract
Purpose
The rapid urbanization of Indian cities and the population surge in cities has steered a massive demand for energy, thereby increasing the carbon emissions in the environment. Information and technology advancements, aided by predictive tools, can optimize this energy demand and help reduce harmful carbon emissions. Out of the multiple factors governing the energy consumption and comfort of buildings, indoor room temperature is a critical one, as it envisages the need for regulating the temperature. This paper aims to propose a mathematical model for short-term forecasting of indoor room temperature in the Indian context to optimize energy consumption and reduce carbon emissions in the environment.
Design/methodology/approach
A study is conducted to forecast the indoor room temperature of an Indian corporate building structure, based upon various external environmental factors: temperature and rainfall and internal factors like cooling control, occupancy behavior and building characteristics. Expert insight and principal component analysis are applied for appropriate variables selection. The machine learning approach using Box–Jenkins time series models is used for the forecasting of indoor room temperature.
Findings
ARIMAX model, with lagged forecasted and explanatory variables, is found to be the best-fit model. A predictive short-term hourly temperature forecasting model is developed based upon ARIMAX model, which yields fairly accurate results for data set pertaining to the building conditions and climatic parameters in the Indian context. Results also investigate the relationships between the forecasted and individual explanatory variables, which are validated using theoretical proofs.
Research limitations/implications
The models considered in this research are Box–Jenkins models, which are linear time series models. There are non-linear models, such as artificial neural network models and deep learning models, which can be a part of this study. The study of hybrid models including combined forecasting techniques comprising linear and non-linear methods is another important area for future scope of study. As this study is based on a single corporate entity, the models developed need to be tested further for robustness and reliability.
Practical implications
Forecasting of indoor room temperature provides essential practical information about meeting the in-future energy demand, that is, how much energy resources would be needed to maintain the equilibrium between energy consumption and building comfort. In addition, this forecast provides information about the prospective peak usage of air-conditioning controls within the building indoor control management system through a feedback control loop. The resultant model developed can be adopted for smart buildings within Indian context.
Social implications
This study has been conducted in India, which has seen a rapid surge in population growth and urbanization. Being a developing country, India needs to channelize its energy needs judiciously by minimizing the energy wastage and reducing carbon emissions. This study proposes certain pre-emptive measures that help in minimizing the consumption of available energy resources as well as reducing carbon emissions that have significant impact on the society and environment at large.
Originality/value
A large number of factors affecting the indoor room temperature present a research challenge for model building. The paper statistically identifies the parameters influencing the indoor room temperature forecasting and their relationship with the forecasted model. Considering Indian climatic, geographical and building structure conditions, the paper presents a systematic mathematical model to forecast hourly indoor room temperature for next 120 h with fair degree of accuracy.
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Sattar Khan, Naimat Ullah Khan and Yasir Kamal
This paper aims to examine the role of corporate governance (CG) in the earnings management (EM) of affiliated companies in family business groups (FBGs) listed on the Pakistan…
Abstract
Purpose
This paper aims to examine the role of corporate governance (CG) in the earnings management (EM) of affiliated companies in family business groups (FBGs) listed on the Pakistan Stock Exchange (PSX), using principal–principal agency theory.
Design/methodology/approach
The sample of 327 nonfinancial firms of the PSX, consisting of 187 group-affiliated firms and 140 nonaffiliated firms has been used in this study for the period of 2010 to 2019. The study uses different regression models for analysis, with robustness tests of various alternative measures of EM and FBG affiliation. In addition, endogeneity is controlled with the propensity score matching method.
Findings
The findings show that EM is less prevalent in affiliated firms compared to nonaffiliated companies. The results show a negative and significant relationship between FBGs affiliated firms and EM. Moreover, the results also show a positive relationship between EM and the interaction term of the CG index and group affiliation. It refers to the fact that effective governance cannot reduce EM in affiliated companies of FBGs as well as in the nonfinancial companies of the PSX. In addition, the quality of CG is higher in affiliated companies compared to its counterpart in nonaffiliated firms. The findings support the principal–principal agency theory that CG cannot mitigate the expropriating behavior of controlling shareholders against minority shareholders by reducing EM in emerging markets due to the ownership concentration phenomenon.
Research limitations/implications
This research study has implications for small investors, government agencies and regulators. The findings of the study show that CG code should make it mandatory for companies to reveal information about their complex ownership structure and ownership information about affiliated companies and directors. Furthermore, it is suggested to revisit the code of CG in the Pakistani context of principal–principal conflict instead of the agent–principal explanation of agency theory based on Anglo–Saxon countries.
Originality/value
This research study has contributed to the CG and FBG literature in relation to EM in idiosyncratic settings of Pakistan. One of the prime contributions of the paper is the development of a comprehensive CG index. This research study used detailed, manually collected novel data on affiliated firms of FBGs in Pakistan.
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Rizky Yudaruddin, Dadang Lesmana, Yanzil Azizil Yudaruddin, Norliza Che Yahya and Ayesha Anwar
This study aims to investigate the market reaction in the cyclical consumer sector to the US–Houthi conflict. Furthermore, the authors explore the impact of this conflict on…
Abstract
Purpose
This study aims to investigate the market reaction in the cyclical consumer sector to the US–Houthi conflict. Furthermore, the authors explore the impact of this conflict on market reactions by market and region.
Design/methodology/approach
Using an event study methodology, this paper analyze a sample of 1,973 companies. This paper used multiple event windows, including a 15-day period before the invasion announcement as the preinvasion event and a 15-day period after the invasion announcement as the postinvasion event.
Findings
The authors find that pre the event of war, the market tended to show a positive reaction, but toward the event day until post event, the market in the consumer cyclical sector actually reacted significantly negatively to the conflict, especially in developed and developing markets. The Asia and Pacific market is the market that feels the most negative impact from the US–Houthi conflict compared to other markets. Furthermore, in terms of industry types in the consumer staples sector, Food and Tobacco and Personal and Household Products and Services felt the negative impact, although the majority of all industries reacted significantly negatively.
Originality/value
This study focuses on the US–Houthi conflict, an event that has not been extensively studied in the context of market reactions. Unlike previous research, this study specifically examines the impact of the conflict on the consumer cyclical sector, emphasizing the significance of trade route disruptions, particularly the Suez Canal, on global markets. By providing insights into how such geopolitical events affect different regions and industries, this study offers valuable guidance for policymakers and managers in mitigating the adverse effects of geopolitical risks on market stability.
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Vineeta Kumari, Dharen Kumar Pandey, Satish Kumar and Emma Xu
The study aims to examine the impact of six events related to the escalating Indo-China border conflicts in 2020 on the Indian stock market, including the role of firm-specific…
Abstract
Purpose
The study aims to examine the impact of six events related to the escalating Indo-China border conflicts in 2020 on the Indian stock market, including the role of firm-specific variables.
Design/methodology/approach
This study employs an event-study method on a sample of 481 firms from August 23, 2019 to March 3, 2022. A cross-sectional regression is employed to examine the association between event-led abnormal returns and firm characteristics.
Findings
The results show that, although the individual events reflect heterogeneous effects on stock market returns, the average impact of the event categories is negative. The study also found that net working capital, current ratio, financial leverage and operating cash flows are significant financial performance indicators and drive cumulative abnormal returns. Further, size anomaly is absent, indicating that more prominent firms are resilient to new information.
Research limitations/implications
The ongoing conflict between Russia and Ukraine is an example of how these disagreements can devolve into a disaster for the parties to the war. Although wars have an impact on markets at the global level, the impacts of border disputes are local. Border disputes are ongoing, and the study's findings can be used to empower investors to make risk-averting decisions that make their portfolios resilient to such events.
Originality/value
This study provides firm-level insight into the impacts of border conflicts on stock markets. The authors compare the magnitude of such impacts on two types of events, namely injuries and casualties due to country-specific border tensions and a government ban on Chinese apps. Key implications for policymakers, stakeholders and academics are presented.
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Mansi Mansi, Rakesh Pandey and Ehtasham Ghauri
This study aims to explore the weightage rendered to corporate social responsibility (CSR) keywords in mission and vision (M&V) statements of public sector enterprises (PSEs) in…
Abstract
Purpose
This study aims to explore the weightage rendered to corporate social responsibility (CSR) keywords in mission and vision (M&V) statements of public sector enterprises (PSEs) in India.
Design/methodology/approach
Analysing the contents of M&V statements of 230 PSEs, this study has the twin research objectives of seeking to illuminate the current use of CSR-related keywords in PSEs’ M&V statements that reflect organisational strategy and provide an understanding for how firm age, industry and firm size variables serve to influence CSR keyword reporting in these statements.
Findings
The findings of this study provide evidence that half of the Indian PSEs reported at least one CSR-related keyword in their M&V statements. These public enterprises predominantly use 38 different categories of CSR keywords in their M&V statements. Furthermore, the authors find that environment-related keywords were predominantly used by PSEs in their M&V statements. The results indicate that PSEs’ size and industries are significantly associated with the use of CSR-related keywords in M&V statements, suggesting that bigger PSEs and PSEs in extractive industries (e.g. mining, coal and petroleum) tend to report more CSR-related keywords in their M&V statements.
Research limitations/implications
Findings imply that small public enterprises (those having a low annual turnover) lack CSR focus in their M&V statements. The authors argue that, irrespective of the size of the enterprise, CSR should be an integral part of these PSEs in framing their M&V statements.
Originality/value
This study systematically analyses CSR-related keywords in the M&V statements of all PSEs in India.
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Rizky Yudaruddin, Dadang Lesmana, Yanzil Azizil Yudaruddin, İbrahim Halil Ekşi̇ and Berna Doğan Başar
This study aims to examine market reactions to the Israel–Hamas conflict in neighboring countries, particularly focusing on the Middle East North Africa (MENA) region.
Abstract
Purpose
This study aims to examine market reactions to the Israel–Hamas conflict in neighboring countries, particularly focusing on the Middle East North Africa (MENA) region.
Design/methodology/approach
The study adopts an event study methodology, employing average abnormal return (AAR) and cumulative abnormal return as measures to assess market reactions. The sample for this study comprises 1,314 companies, with October 9, 2023, identified as the event day for analysis.
Findings
The results of our study indicate that countries in close proximity to Israel and Palestine encountered detrimental effects on their capital markets, as evidenced by negative responses observed across various sectors. Our analysis also reveals that countries in the midst of conflict, particularly Israel, experienced a decrease in their stock markets across various sectors, with the exception of materials and real estate. In addition, our investigation reveals disparities in market responses according to different categories of company size.
Originality/value
This research is the first to study market reactions to Israel–Hamas in the MENA region at the company level.
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Mithilesh Pandey and Rajesh Poonia
The learning outcomes are as follows: to familiarize students with the concept of segmentation, targeting and positioning; to make students understand the need and process of…
Abstract
Learning outcomes
The learning outcomes are as follows: to familiarize students with the concept of segmentation, targeting and positioning; to make students understand the need and process of building a brand; to help students to identify the market gap and meet customer’s requirement by delivering the right value proposition; and to examine the feasibility of business opportunity, develop a business plan and run a successful firm.
Case overview/synopsis
This case is about the quest of three MBA students who accidentally get into argument about footwear brands. This argument leads them to Punjabi ethnic footwear popularly known as “Punjabi Jutti.” They decide to understand the background of “Punjabi Jutti” and the possibility of developing a brand for the same. An extensive research was carried out through the various online and offline platforms. The research included searching through the existing literature, collecting data from the various online platforms such as e-commerce websites and interviews from the field. The research revealed that this traditional artwork is an unorganized sector. The manufacturers and marketers are two main parts of this business. However, the mainstay of the business is the skilled labors who know the art of making “Punjabi Jutti.” This art has been inherited by them from their previous generations. Also, it was found that there was good demand of the “Punjabi Jutti” in India and it was exported to various countries as well. Customers had a mix response toward these products. This extensive research has now put these students in a dilemma as to what should be the next step. Should they step into this business by creating their own brand? Will this entrepreneurship venture be sustainable? If they created a brand for the “Punjabi Jutti” then what kind of brand it would be?
Complexity academic level
Post graduate, entrepreneurs.
Supplementary materials
Teaching Notes are available for educators only.
Subject code
CSS 3: Entrepreneurship.
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Mariem Ben Abdallah and Slah Bahloul
The objective of this research is to determine the influence of solvency and liquidity on the profitability [return on assets (ROA)] of Tunisian banks from Q2-2020 to Q3-2022 by…
Abstract
Purpose
The objective of this research is to determine the influence of solvency and liquidity on the profitability [return on assets (ROA)] of Tunisian banks from Q2-2020 to Q3-2022 by considering asset quality as a moderating variable.
Design/methodology/approach
This study uses data on liquidity, solvency, ROA and asset quality for 12 banks. It also considers bank size, gross domestic product (GDP) growth and inflation as control variables. The methodology is based on panel data with generalized least squares (GLS) estimation to assess the moderate influence of the asset quality on solvency, liquidity and ROA. Also, the generalized method of moments (GMM) estimation is used as a robustness test.
Findings
The results of the GLS model estimation indicated a negatively significant moderating correlation between the liquidity and the solvency. The data from the GMM model indicate that the liquidity variable predicted by the liquidity has a positively significant influence on a bank's ROA as well as for the solvency variable, which is predicted by the capital capacity. Therefore, we conclude that these two variables had a positively significant impact on the ROA.
Research limitations/implications
The studies have many implications for banks and their management in addition to the industry regulators. The results of this study will enable political decision-makers to determine the banks' profits based on their liquidity and solvency.
Originality/value
This analysis provides financial explanations and recommendations for stakeholders in Tunisian banks. Furthermore, these banks must also be able to maintain their liquidity and solvency to ensure their profits in times of COVID-19.