Weather Dataset (Malaysia & Nigeria)

Power Line Communication (PLC) is a network technology that supports simultaneous deliveryofelectricityanddataoverpowerlines.Thepowerinfrastructuresareprimarilydesignedtodeliver electricity; and can be utilized to transmit conventional communication data upon deployment of PLC system.Climatechangemakesweathereventsbecomemorevaryingandextremes.Suchevents(temperature, humidity, storms, wind, pressure, rain, etc.) can potentially influence reliability of data transmission of weather-exposed PLC-based power lines. This paper, investigates and analyses the potential impact of multipleweathereventsonPLC-basedsystemregarding‘throughput’.Fortheanalysis,itproposesaMulti- linearRegressionModel(MRM)developedfromNeuralNetworkRegression(NNR)techniqueofMachine Learning(ML) topredict regionalperformance of‘throughput’ inMalaysia andNigeria. Thepredictionis evaluatedusingtheITU–TG.9960(20019–2023)standardthatrecommends85–98%‘throughput’perfor- mance for wireline-based technologies like the PLC. Related works from US and China regions were also considered for comparison and evaluation. The regional prediction on ‘throughput’ performance obtained forMalaysiais71.62%,whileforNigeria’sNortheast,Northwest,Northcentral,Southeast,Southwest,and Southsouth are; 99.97%, 79.85%, 96.59%, 81.89%, 86.32%, and 74.46%; respectively. Prediction below the G.9960 recommendation indicates that regional weather events are not favourable for PLC-based data transmission,andcanpotentiallyinfluenceit.Asingleweatherevent(wind)wasinvestigatedandanalyzedin theEasternregionofChina,wheretheanalysisachieved0.995onthefitnessofdata(R–squared)throughML process.Thispaperachievesregionalfitnessofdataasfollows:Malaysia(0.985),andNigeria’sNortheast, Northwest, Northcentral, Southwest, Southeast, and Southsouth; 0.980, 0.953, 0.966, 0.984, 0.989, and 0.978 respectively. Another event (thunderstorms) was regressively examined to determine its impact on powerlinesacrosstheSouthwestUSwhereitachieved0.630ondatafitness.Onthesameevent,thispaper achieves regional fitness as follows; Malaysia (0.970), and Nigeria’s Northeast, Northwest, Northcentral, Southwest,Southeast,andSouthsouthas;0.980,0.870,0.907,0.956,0.971,and0.940respectively.TheR- square value (0 – 1) indicates reliability of ML-based analysis by describing suitability and quality of data intheprocess.Thispaperoffersthefollowing;investigatesandanalysestheinfluenceofregionalweather on PLC-based transmission; provides weather-based references to power, communication, and regulatory industriesontheimplementationandmaintenanceofPLC-basednetworks;providespotentialfordeploying the analysis to other communication technologies and regions beyond; provides opportunities to optimize weather-dependent PLC and other related communication networks.