A small-scale experimental salinity-gradient solar pond, which will be utilized for the research and development in harnessing solar energy for desalination of seawater and generation of electricity, has been constructed. The pond has effective length, width and depth of 3.0 m, 2.0 m and 2.0 m, respectively, covering a volume capacity of 12.0 m 3 . Thermal insulation plays a major role for the successful operation of a salinity-gradient solar pond, especially when the dimensions of the pond are relatively small. The construction details of the solar pond, with particular attention to the methodologies adapted for the thermal insulation, are reported in the present work. The expected total rate of heat loss due to conduction through the thermally insulated boundary walls, assuming a bottom temperature of 90 ºC, has been calculated and found to be 106.3 W. Contribution from the bottom convective zone itself to this total rate of heat loss is 69 W, which corresponds to 65% of the total value. Based on this rate, the estimated temperature drop during the period with no solar radiation present in a typical day is only 0.3 ºC. With such a small temperature drop, it is possible to extract the thermal energy stored in the bottom convective zone during the day time, continuously, while maintaining the stability of the solar pond.

The thermo-foil is an insulation material which can be used to insulate roofs of residential buildings which causes less energy to accomplish a comfortable temperature inside the building. It's becoming popular in Sri Lankan building construction sector. In this research, an investigation of the efficiency of the thermo-foils is done for commonly used brands. The important thermal properties are analyzed for thermo-foils with a thickness of 3mm, 5mm and 8mm and it is tested using a miniature building model for the further evaluation of the efficiency. It was observed that the aluminium layer of the thermo-foil could reflect most of the radiation back and the polyethylene foam layer could keep a temperature gradient of a few degrees. A temperature drop of 1.6 to 2.8 degrees was noted. The results obtained were discussed with an analysis of the thermal insulation properties. A heat transfer model for a residential building was also proposed.

In this empirical study, socioeconomic factors that can easily be extracted from families have been used to build a "home electricity usage prediction" model based on two variables, family monthly income and family size. Each of these factors was evaluated individually. Two machine learning models were built using those factors as features. Two new parameters were introduced to analyze the data. Models are based on "Linear Regression" and "Random Forest" algorithms. This study revealed that the socioeconomic factors such as family size and family income are very effective in domestic electricity usage prediction model building, where the end usages are not known. Furthermore, the random forest algorithm was found to be more effective for unseen data than the linear regression algorithm. The accuracy of the models can be further improved by adding more data into the both models.

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