Browsing by Author "Dissanayake, BI"

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item: Conference-Full-text
Fuel stacking and stove choice decisions: a discrete choice model of Sri Lankan household preferences for clean cooking solution
(IEEE, 2022-07) Dissanayake, BI; Perera, HN; De Silva, MM; Rathnayake, M; Adhikariwatte, V; Hemachandra, K
Transition from non-clean cooking fuels to clean cooking fuels is a sustainable development requirement which needs to be addressed at a household level. Household decisions play a crucial role in terms of cooking transition and preference towards product specific factors. Developing countries need to address this problem immediately as it faces socioeconomic challenges which are complex. The presence of improved consumer products according to consumer’s preference for cooking fuels in these regions acts as a major motivator for cookstove adoption. Hence, understanding household preference for clean cooking solutions is useful for policy makers and organisations involved with the cooking fuel supply chain and product development. In order to better understand household decision-making in Sri Lanka, a stated preference survey and discrete choice model were constructed. With the exception of price and usage cost factors for the dirty stacking households, the study indicated that the product-specific factors explored have a significant impact on stove and fuel choices. Instead, energy switching, in which LPG and electricity supplement and increase a household's energy portfolio, is more likely.
item: Conference-Full-text
Review of methodologies used in electricity supply and demand forecasting
(Sri Lanka Society of Transport and Logistics, 2023-08-26) Dissanayake, BI; Perera, HN; Velmanickam, L; Gunaruwan, TL
European countries began liberalizing their electricity markets to increase competition and reduce prices for consumers [1]. In a liberalized electricity market, electricity is treated as a tradable commodity like any other product. Since then, electricity markets have been subject to the same economic principles of supply and demand as other markets, with prices rising when demand outstrips supply and falling when supply exceeds demand. A variety of methods and ideas have been tried for electricity forecasting in generation, demand, and price domains over the last few decades, with varying degrees of success. Over time. Researchers have applied methodologies from time series analysis, ARIMA models to machine learning and deep learning techniques. The evolution of these techniques have improved cost reductions in the industry. The purpose of this review is to illustrate the evolution of employed methodology, the complexity of applied solutions, and the opportunities and challenges that forecasting tools offer or may encounter.